Journal articles: 'Summarization'

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da Cunha, Iria, Leo Wanner, and Teresa Cabré. "Summarization of specialized discourse." Terminology 13, no. 2 (November 19, 2007): 249–86. http://dx.doi.org/10.1075/term.13.2.07cun.

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In this article, we present the current state of our work on a linguistically-motivated model for automatic summarization of medical articles in Spanish. The model takes into account the results of an empirical study which reveals that, on the one hand, domain-specific summarization criteria can often be derived from the summaries of domain specialists, and, on the other hand, adequate summarization strategies must be multidimensional, i.e., cover various types of linguistic clues. We take into account the textual, lexical, discursive, syntactic and communicative dimensions. This is novel in the field of summarization. The experiments carried out so far indicate that our model is suitable to provide high quality summarizations.

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Sirohi, Neeraj Kumar, Dr Mamta Bansal, and Dr S. N. Rajan Rajan. "Text Summarization Approaches Using Machine Learning & LSTM." Revista Gestão Inovação e Tecnologias 11, no. 4 (September 1, 2021): 5010–26. http://dx.doi.org/10.47059/revistageintec.v11i4.2526.

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Due to the massive amount of online textual data generated in a diversity of social media, web, and other information-centric applications. To select the vital data from the large text, need to study the full article and generate summary also not loose critical information of text document this process is called summarization. Text summarization is done either by human which need expertise in that area, also very tedious and time consuming. second type of summarization is done through system which is known as automatic text summarization which generate summary automatically. There are mainly two categories of Automatic text summarizations that is abstractive and extractive text summarization. Extractive summary is produced by picking important and high rank sentences and word from the text document on the other hand the sentences and word are present in the summary generated through Abstractive method may not present in original text. This article mainly focuses on different ATS (Automatic text summarization) techniques that has been instigated in the present are argue. The paper begin with a concise introduction of automatic text summarization, then closely discussed the innovative developments in extractive and abstractive text summarization methods, and then transfers to literature survey, and it finally sum-up with the proposed techniques using LSTM with encoder Decoder for abstractive text summarization are discussed along with some future work directions.

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Blekanov, Ivan S., Nikita Tarasov, and Svetlana S. Bodrunova. "Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages." Future Internet 14, no. 3 (February 23, 2022): 69. http://dx.doi.org/10.3390/fi14030069.

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Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual analysis. Of all social media, Reddit is most frequently used for testing new neural models of text summarization on large-scale datasets in English, without further testing on real-world smaller-size data in various languages or from various other platforms. Moreover, for social media, summarizing pools of texts (one-author posts, comment threads, discussion cascades, etc.) may bring crucial results relevant for social studies, which have not yet been tested. However, the existing methods of abstractive summarization are not fine-tuned for social media data and have next-to-never been applied to data from platforms beyond Reddit, nor for comments or non-English user texts. We address these research gaps by fine-tuning the newest Transformer-based neural network models LongFormer and T5 and testing them against BART, and on real-world data from Reddit, with improvements of up to 2%. Then, we apply the best model (fine-tuned T5) to pools of comments from Reddit and assess the similarity of post and comment summarizations. Further, to overcome the 500-token limitation of T5 for analyzing social media pools that are usually bigger, we apply LongFormer Large and T5 Large to pools of tweets from a large-scale discussion on the Charlie Hebdo massacre in three languages and prove that pool summarizations may be used for detecting micro-shifts in agendas of networked discussions. Our results show, however, that additional learning is definitely needed for German and French, as the results for these languages are non-satisfactory, and more fine-tuning is needed even in English for Twitter data. Thus, we show that a ‘one-for-all’ neural-network summarization model is still impossible to reach, while fine-tuning for platform affordances works well. We also show that fine-tuned T5 works best for small-scale social media data, but LongFormer is helpful for larger-scale pool summarizations.

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Pei, Jisheng, and Xiaojun Ye. "Information-Balance-Aware Approximated Summarization of Data Provenance." Scientific Programming 2017 (September 12, 2017): 1–11. http://dx.doi.org/10.1155/2017/4504589.

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Extracting useful knowledge from data provenance information has been challenging because provenance information is often overwhelmingly enormous for users to understand. Recently, it has been proposed that we may summarize data provenance items by grouping semantically related provenance annotations so as to achieve concise provenance representation. Users may provide their intended use of the provenance data in terms of provisioning, and the quality of provenance summarization could be optimized for smaller size and closer distance between the provisioning results derived from the summarization and those from the original provenance. However, apart from the intended provisioning use, we notice that more dedicated and diverse user requirements can be expressed and considered in the summarization process by assigning importance weights to provenance elements. Moreover, we introduce information balance index (IBI), an entropy based measurement, to dynamically evaluate the amount of information retained by the summary to check how it suits user requirements. An alternative provenance summarization algorithm that supports manipulation of information balance is presented. Case studies and experiments show that, in summarization process, information balance can be effectively steered towards user-defined goals and requirement-driven variants of the provenance summarizations can be achieved to support a series of interesting scenarios.

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Bhatia, Neelima, and Arunima Jaiswal. "Literature Review on Automatic Text Summarization: Single and Multiple Summarizations." International Journal of Computer Applications 117, no. 6 (May 20, 2015): 25–29. http://dx.doi.org/10.5120/20560-2948.

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Zhang, Qianjin, Dahai Jin, Yawen Wang, and Yunzhan Gong. "Statement-Grained Hierarchy Enhanced Code Summarization." Electronics 13, no. 4 (February 15, 2024): 765. http://dx.doi.org/10.3390/electronics13040765.

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Code summarization plays a vital role in aiding developers with program comprehension by generating corresponding textual descriptions for code snippets. While recent approaches have concentrated on encoding the textual and structural characteristics of source code, they often neglect the global hierarchical features, causing limited code representation. Addressing this gap, our paper introduces the statement-grained hierarchy enhanced Transformer model (SHT), a novel framework that integrates global hierarchy, syntax, and token sequences to automatically generate summaries for code snippets. SHT is distinctively designed with two encoders to learn both hierarchical and sequential features of code. One relational attention encoder processes the statement-grained hierarchical graph, producing hierarchical embeddings. Subsequently, another sequence encoder integrates these hierarchical structures with token sequences. The resulting enriched representation is then fed into a vanilla Transformer decoder, which effectively generates concise and informative summarizations. Our extensive experiments demonstrate that SHT significantly outperforms state-of-the-art approaches on two widely used Java benchmarks. This underscores the effectiveness of incorporating global hierarchical information in enhancing the quality of code summarizations.

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S, Sai Shashank, Sindhu S, Vineeth V, and Pranathi C. "VIDEO SUMMARIZATION." International Research Journal of Computer Science 9, no. 8 (August 13, 2022): 277–80. http://dx.doi.org/10.26562/irjcs.2022.v0908.24.

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The general public now has access to a vast amount of multimedia information thanks to recent technological advancements and the quick expansion of consumer electronics, making it challenging to effectively consume video material among the thousands of options accessible. By choosing and presenting the most educational or fascinating materials for users, we provide a method to quickly summarize the content of a lengthy video document. The practice of condensing a raw video into a more manageable form without losing much information is known as video summarizing. Either a comprehensive analysis of the full movie or the local differences between neighboring frames are used to achieve this. The majority of such approaches rely on universal characteristics like color, texture, motion data, etc. Video summaries are evaluated depending on the sort of content they are formed from (object, event, perception, or feature-based) and the functionality made available to the user for consumption (interactive or static, personalized or generic). The suggested system analyses each frame of a video as input before producing a summary. Each frame receives a score that is used to compare it to a threshold value in the final phase. Every frame whose frame score exceeds the threshold is chosen as a key frame and is represented in the final movie summary. This technique enables us to condense video information of various lengths while guaranteeing that the key moments are included. The purpose of video summary is to facilitate quick access, speed up browsing through a sizable video database, and offer a condensed video representation while maintaining the core activities of the original video.

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Nenkova, Ani. "Automatic Summarization." Foundations and Trends® in Information Retrieval 5, no. 2 (2011): 103–233. http://dx.doi.org/10.1561/1500000015.

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Larson, Martha. "Automatic Summarization." Foundations and Trends® in Information Retrieval 5, no. 3 (2012): 235–422. http://dx.doi.org/10.1561/1500000020.

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D, Manju, Radhamani V, Dhanush Kannan A, Kavya B, Sangavi S, and Swetha Srinivasan. "TEXT SUMMARIZATION." YMER Digital 21, no. 07 (July 7, 2022): 173–82. http://dx.doi.org/10.37896/ymer21.07/13.

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n the last few years, a huge amount of text data from different sources has been created every day. The enormous data which needs to be processed contains valuable detail which needs to be efficiently summarized so that it serves a purpose. It is very tedious to summarize and classify large amounts of documents when done manually. It becomes cumbersome to develop a summary taking every semantics into consideration. Therefore, automatic text summarization acts as a solution. Text summarization can help in understanding the huge corpus by providing a gist of the corpus enabling comprehension in a timely manner. This paper studies the development of a web application which summarizes the given input text using different models and its deployment. Keywords: Text summarization, NLP, AWS, Text mining

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Maña-López, Manuel J., Manuel De Buenaga, and José M. Gómez-Hidalgo. "Multidocument summarization." ACM Transactions on Information Systems 22, no. 2 (April 2004): 215–41. http://dx.doi.org/10.1145/984321.984323.

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Vikas, A., Pradyumna G.V.N, and Tahir Ahmed Shaik. "Text Summarization." International Journal of Engineering and Computer Science 9, no. 2 (February 3, 2020): 24940–45. http://dx.doi.org/10.18535/ijecs/v9i2.4437.

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In this new era, where tremendous information is available on the internet, it is most important to provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of a large documents of text. There are plenty of text material available on the internet. So, there is a problem of searching for relevant documents from the number of documents available and absorbing relevant information from it. In order to solve the above two problems, the automatic text summarization is very much necessary. Text summarization is the process of identifying the most important meaningful information in a document or set of related documents and compressing them into a shorter version preserving its overall meanings.

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Balaji, J., T. V. Geetha, and Ranjani Parthasarathi. "Abstractive Summarization." International Journal on Semantic Web and Information Systems 12, no. 2 (April 2016): 76–99. http://dx.doi.org/10.4018/ijswis.2016040104.

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Customization of information from web documents is an immense job that involves mainly the shortening of original texts. This task is carried out using summarization techniques. In general, an automatically generated summary is of two types – extractive and abstractive. Extractive methods use surface level and statistical features for the selection of important sentences, without considering the meaning conveyed by those sentences. In contrast, abstractive methods need a formal semantic representation, where the selection of important components and the rephrasing of the selected components are carried out using the semantic features associated with the words as well as the context. Furthermore, a deep linguistic analysis is needed for generating summaries. However, the bottleneck behind abstractive summarization is that it requires semantic representation, inference rules and natural language generation. In this paper, The authors propose a semi-supervised bootstrapping approach for the identification of important components for abstractive summarization. The input to the proposed approach is a fully connected semantic graph of a document, where the semantic graphs are constructed for sentences, which are then connected by synonym concepts and co-referring entities to form a complete semantic graph. The direction of the traversal of nodes is determined by a modified spreading activation algorithm, where the importance of the nodes and edges are decided, based on the node and its connected edges under consideration. Summary obtained using the proposed approach is compared with extractive and template based summaries, and also evaluated using ROUGE scores.

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Jha, Nitesh Kumar, and Arnab Mitra. "Introducing Word's Importance Level-Based Text Summarization Using Tree Structure." International Journal of Information Retrieval Research 10, no. 1 (January 2020): 13–33. http://dx.doi.org/10.4018/ijirr.2020010102.

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Text-summarization plays a significant role towards quick knowledge acquisition from any text-based knowledge resource. To enhance the text-summarization process, a new approach towards automatic text-summarization is presented in this article that facilitates level (word importance factor)-based automated text-summarization. An equivalent tree is produced from the directed-graph during the input text processing with WordNet. Detailed investigations further ensure that the execution time for proposed automatic text-summarization, is strictly following a linear relationship with reference to the varying volume of inputs. Further investigation towards the performance of proposed automatic text-summarization approach ensures its superiority over several other existing text-summarization approaches.

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Lucky, Henry, and Derwin Suhartono. "Investigation of Pre-Trained Bidirectional Encoder Representations from Transformers Checkpoints for Indonesian Abstractive Text Summarization." Journal of Information and Communication Technology 21, No.1 (November 11, 2021): 71–94. http://dx.doi.org/10.32890/jict2022.21.1.4.

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Text summarization aims to reduce text by removing less useful information to obtain information quickly and precisely. In Indonesian abstractive text summarization, the research mostly focuses on multi-document summarization which methods will not work optimally in single-document summarization. As the public summarization datasets and works in English are focusing on single-document summarization, this study emphasized on Indonesian single-document summarization. Abstractive text summarization studies in English frequently use Bidirectional Encoder Representations from Transformers (BERT), and since Indonesian BERT checkpoint is available, it was employed in this study. This study investigated the use of Indonesian BERT in abstractive text summarization on the IndoSum dataset using the BERTSum model. The investigation proceeded by using various combinations of model encoders, model embedding sizes, and model decoders. Evaluation results showed that models with more embedding size and used Generative Pre-Training (GPT)-like decoder could improve the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score and BERTScore of the model results.

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Parimoo, Rohit, Rohit Sharma, Naleen Gaur, Nimish Jain, and Sweeta Bansal. "Applying Text Rank to Build an Automatic Text Summarization Web Application." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 865–67. http://dx.doi.org/10.22214/ijraset.2022.40766.

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Abstract: Automatic Text Summarization is one of the most trending research areas in the field of Natural Language Processing. The main aim of text summarization is to reduce the size of a text without losing any important information. Various techniques can be used for automatic summarization of text. In this paper we are going to focus on the automatic summarization of text using graph-based methods. In particular, we are going to discuss the implementation of a general-purpose web application which performs automatic summarization on the text entered using the Text Rank Algorithm. Summarization of text using graph-based approaches involves pre-processing and cleansing of text, tokenizing the sentences present in the text, representing the tokenized text in the form of numerical vectors, creating a similarity matrix which shows the semantic similarity between different sentences present in the text, representing the similarity matrix as a graph, scoring and ranking the sentences and extracting the summary. Keywords: Text Summarization, Unsupervised Learning, Text Rank, Page Rank, Web Application, Graph Based Summarization, Extractive Summarization

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Diedrichsen, Elke. "Linguistic challenges in automatic summarization technology." Journal of Computer-Assisted Linguistic Research 1, no. 1 (June 26, 2017): 40. http://dx.doi.org/10.4995/jclr.2017.7787.

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Automatic summarization is a field of Natural Language Processing that is increasingly used in industry today. The goal of the summarization process is to create a summary of one document or a multiplicity of documents that will retain the sense and the most important aspects while reducing the length considerably, to a size that may be user-defined. One differentiates between extraction-based and abstraction-based summarization. In an extraction-based system, the words and sentences are copied out of the original source without any modification. An abstraction-based summary can compress, fuse or paraphrase sections of the source document. As of today, most summarization systems are extractive. Automatic document summarization technology presents interesting challenges for Natural Language Processing. It works on the basis of coreference resolution, discourse analysis, named entity recognition (NER), information extraction (IE), natural language understanding, topic segmentation and recognition, word segmentation and part-of-speech tagging. This study will overview some current approaches to the implementation of auto summarization technology and discuss the state of the art of the most important NLP tasks involved in them. We will pay particular attention to current methods of sentence extraction and compression for single and multi-document summarization, as these applications are based on theories of syntax and discourse and their implementation therefore requires a solid background in linguistics. Summarization technologies are also used for image collection summarization and video summarization, but the scope of this paper will be limited to document summarization.

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Howlader, Prottyee, Prapti Paul, Meghana Madavi, Laxmi Bewoor, and V. S. Deshpande. "Fine Tuning Transformer Based BERT Model for Generating the Automatic Book Summary." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (December 15, 2022): 347–52. http://dx.doi.org/10.17762/ijritcc.v10i1s.5902.

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Major text summarization research is mainly focusing on summarizing short documents and very few works is witnessed for long document summarization. Additionally, extractive summarization is more addressed as compared with abstractive summarization. Abstractive summarization, unlike extractive summarization, does not only copy essential words from the original text but requires paraphrasing to get close to human generated summary. The machine learning, deep learning models are adapted to contemporary pre-trained models like transformers. Transformer based Language models gaining a lot of attention because of self-supervised training while fine-tuning for Natural Language Processing (NLP) downstream task like text summarization. The proposed work is an attempt to investigate the use of transformers for abstraction. The proposed work is tested for book especially as a long document for evaluating the performance of the model.

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Tahseen, Rabia, Uzma Omer, Muhammad Shoaib Farooq, and Faiqa Adnan. "Text Summarization Techniques Using Natural Language Processing: A Systematic Literature Review." VFAST Transactions on Software Engineering 9, no. 4 (December 31, 2021): 102–8. http://dx.doi.org/10.21015/vtse.v9i4.856.

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In recent years, data has been growing rapidly in almost every domain. Due to this excessiveness of data, there is a need for an automatic text summarizer that summarizes long and numerical data especially textual data without losing its content. Text summarization has been under research for decades and researchers used different summarization methods by using natural language processing and combining various algorithms. This paper presents a systematic literature review by showing a survey of text summarization methods and explains the accuracy of these methods used for text summarization. The paper first introduced some concepts of extractive and abstractive text summarization and also define how deep learning models can be used for the improvement of text summarization. This paper aims to identify the current utilization of text summarization in different application domains. Different methodologies are discussed for text summarization. To carry out this SLR, twenty-four published articles have been chosen carefully for this domain. Moreover, it discusses issues and challenges which are investigated in different application domains using text summarization methods. Lastly, the existing work of different researchers has been carried out for further discussion.

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Chang, Hsien-Tsung, Shu-Wei Liu, and Nilamadhab Mishra. "A tracking and summarization system for online Chinese news topics." Aslib Journal of Information Management 67, no. 6 (November 16, 2015): 687–99. http://dx.doi.org/10.1108/ajim-10-2014-0147.

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Purpose – The purpose of this paper is to design and implement new tracking and summarization algorithms for Chinese news content. Based on the proposed methods and algorithms, the authors extract the important sentences that are contained in topic stories and list those sentences according to timestamp order to ensure ease of understanding and to visualize multiple news stories on a single screen. Design/methodology/approach – This paper encompasses an investigational approach that implements a new Dynamic Centroid Summarization algorithm in addition to a Term Frequency (TF)-Density algorithm to empirically compute three target parameters, i.e., recall, precision, and F-measure. Findings – The proposed TF-Density algorithm is implemented and compared with the well-known algorithms Term Frequency-Inverse Word Frequency (TF-IWF) and Term Frequency-Inverse Document Frequency (TF-IDF). Three test data sets are configured from Chinese news web sites for use during the investigation, and two important findings are obtained that help the authors provide more precision and efficiency when recognizing the important words in the text. First, the authors evaluate three topic tracking algorithms, i.e., TF-Density, TF-IDF, and TF-IWF, with the said target parameters and find that the recall, precision, and F-measure of the proposed TF-Density algorithm is better than those of the TF-IWF and TF-IDF algorithms. In the context of the second finding, the authors implement a blind test approach to obtain the results of topic summarizations and find that the proposed Dynamic Centroid Summarization process can more accurately select topic sentences than the LexRank process. Research limitations/implications – The results show that the tracking and summarization algorithms for news topics can provide more precise and convenient results for users tracking the news. The analysis and implications are limited to Chinese news content from Chinese news web sites such as Apple Library, UDN, and well-known portals like Yahoo and Google. Originality/value – The research provides an empirical analysis of Chinese news content through the proposed TF-Density and Dynamic Centroid Summarization algorithms. It focusses on improving the means of summarizing a set of news stories to appear for browsing on a single screen and carries implications for innovative word measurements in practice.

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Thomas, Sinnu Susan, Sumana Gupta, and Venkatesh K. Subramanian. "Perceptual Video Summarization—A New Framework for Video Summarization." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 8 (August 2017): 1790–802. http://dx.doi.org/10.1109/tcsvt.2016.2556558.

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ber, Bam, and Micah Jason. "News Filtering and Summarization System Architecture for Recognition and Summarization of News Pages." Bonfring International Journal of Data Mining 7, no. 2 (May 31, 2017): 11–15. http://dx.doi.org/10.9756/bijdm.8339.

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Rahamat Basha, S., J. Keziya Rani, and J. J. C. Prasad Yadav. "A Novel Summarization-based Approach for Feature Reduction Enhancing Text Classification Accuracy." Engineering, Technology & Applied Science Research 9, no. 6 (December 1, 2019): 5001–5. http://dx.doi.org/10.48084/etasr.3173.

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Automatic summarization is the process of shortening one (in single document summarization) or multiple documents (in multi-document summarization). In this paper, a new feature selection method for the nearest neighbor classifier by summarizing the original training documents based on sentence importance measure is proposed. Our approach for single document summarization uses two measures for sentence similarity: the frequency of the terms in one sentence and the similarity of that sentence to other sentences. All sentences were ranked accordingly and the sentences with top ranks (with a threshold constraint) were selected for summarization. The summary of every document in the corpus is taken into a new document used for the summarization evaluation process.

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Kopeć, Mateusz. "Three-step coreference-based summarizer for Polish news texts." Poznan Studies in Contemporary Linguistics 55, no. 2 (June 26, 2019): 397–443. http://dx.doi.org/10.1515/psicl-2019-0015.

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Abstract This article addresses the problem of automatic summarization of press articles in Polish. The main novelty of this research lays in the proposal of a three-step summarization algorithm which benefits from using coreference information. In related work section, all coreference-based approaches to summarization are presented. Then we describe in detail all publicly available summarization tools developed for Polish language. We state the problem of single-document press article summarization for Polish, describing the training and evaluation dataset: the POLISH SUMMARIES CORPUS. Next, a new coreference-based extractive summarization system NICOLAS is introduced. Its algorithm utilises advanced third-party preprocessing tools to extract the coreference information from the text to be summarized. This information is transformed into a complex set of features related to coreference concepts (mentions and coreference clusters) that are used for training the summarization system (on the basis of a manually prepared gold summaries corpus). The proposed solution is compared to the best publicly available summarization systems for Polish language and two state-of-the-art tools, developed for English language, but adapted to Polish for this article. NICOLAS summarization system obtains best scores, for selected metrics outperforming other systems in a statistically significant way. The evaluation also contains calculation of interesting upper-bounds: human performance and theoretical upper-bound.

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Riahi Samani, Zahra, and Mohsen Ebrahimi Moghaddam. "Image Collection Summarization Method Based on Semantic Hierarchies." AI 1, no. 2 (May 18, 2020): 209–28. http://dx.doi.org/10.3390/ai1020014.

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The size of internet image collections is increasing drastically. As a result, new techniques are required to facilitate users in browsing, navigation, and summarization of these large volume collections. Image collection summarization methods present users with a set of exemplar images as the most representative ones from the initial image collection. In this study, an image collection summarization technique was introduced according to semantic hierarchies among them. In the proposed approach, images were mapped to the nodes of a pre-defined domain ontology. In this way, a semantic hierarchical classifier was used, which finally mapped images to different nodes of the ontology. We made a compromise between the degree of freedom of the classifier and the goodness of the summarization method. The summarization was done using a group of high-level features that provided a semantic measurement of information in images. Experimental outcomes indicated that the introduced image collection summarization method outperformed the recent techniques for the summarization of image collections.

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Pivovarov, Rimma, and Noémie Elhadad. "Automated methods for the summarization of electronic health records." Journal of the American Medical Informatics Association 22, no. 5 (April 15, 2015): 938–47. http://dx.doi.org/10.1093/jamia/ocv032.

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Abstract Objectives This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization. We organize the published research and highlight methodological challenges in the area of EHR summarization implementation. Target audience The target audience for this review includes researchers, designers, and informaticians who are concerned about the problem of information overload in the clinical setting as well as both users and developers of clinical summarization systems. Scope Automated summarization has been a long-studied subject in the fields of natural language processing and human–computer interaction, but the translation of summarization and visualization methods to the complexity of the clinical workflow is slow moving. We assess work in aggregating and visualizing patient information with a particular focus on methods for detecting and removing redundancy, describing temporality, determining salience, accounting for missing data, and taking advantage of encoded clinical knowledge. We identify and discuss open challenges critical to the implementation and use of robust EHR summarization systems.

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Ahuir, Vicent, José-Ángel González, Lluís-F. Hurtado, and Encarna Segarra. "Abstractive Summarizers Become Emotional on News Summarization." Applied Sciences 14, no. 2 (January 15, 2024): 713. http://dx.doi.org/10.3390/app14020713.

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Emotions are central to understanding contemporary journalism; however, they are overlooked in automatic news summarization. Actually, summaries are an entry point to the source article that could favor some emotions to captivate the reader. Nevertheless, the emotional content of summarization corpora and the emotional behavior of summarization models are still unexplored. In this work, we explore the usage of established methodologies to study the emotional content of summarization corpora and the emotional behavior of summarization models. Using these methodologies, we study the emotional content of two widely used summarization corpora: Cnn/Dailymail and Xsum, and the capabilities of three state-of-the-art transformer-based abstractive systems for eliciting emotions in the generated summaries: Bart, Pegasus, and T5. The main significant findings are as follows: (i) emotions are persistent in the two summarization corpora, (ii) summarizers approach moderately well the emotions of the reference summaries, and (iii) more than 75% of the emotions introduced by novel words in generated summaries are present in the reference ones. The combined use of these methodologies has allowed us to conduct a satisfactory study of the emotional content in news summarization.

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Zhu, Junnan, Lu Xiang, Yu Zhou, Jiajun Zhang, and Chengqing Zong. "Graph-based Multimodal Ranking Models for Multimodal Summarization." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (May 26, 2021): 1–21. http://dx.doi.org/10.1145/3445794.

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Multimodal summarization aims to extract the most important information from the multimedia input. It is becoming increasingly popular due to the rapid growth of multimedia data in recent years. There are various researches focusing on different multimodal summarization tasks. However, the existing methods can only generate single-modal output or multimodal output. In addition, most of them need a lot of annotated samples for training, which makes it difficult to be generalized to other tasks or domains. Motivated by this, we propose a unified framework for multimodal summarization that can cover both single-modal output summarization and multimodal output summarization. In our framework, we consider three different scenarios and propose the respective unsupervised graph-based multimodal summarization models without the requirement of any manually annotated document-summary pairs for training: (1) generic multimodal ranking, (2) modal-dominated multimodal ranking, and (3) non-redundant text-image multimodal ranking. Furthermore, an image-text similarity estimation model is introduced to measure the semantic similarity between image and text. Experiments show that our proposed models outperform the single-modal summarization methods on both automatic and human evaluation metrics. Besides, our models can also improve the single-modal summarization with the guidance of the multimedia information. This study can be applied as the benchmark for further study on multimodal summarization task.

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Zhang, Mengli, Gang Zhou, Wanting Yu, Ningbo Huang, and Wenfen Liu. "A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (August 1, 2022): 1–21. http://dx.doi.org/10.1155/2022/7132226.

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With the rapid development of the Internet, the massive amount of web textual data has grown exponentially, which has brought considerable challenges to downstream tasks, such as document management, text classification, and information retrieval. Automatic text summarization (ATS) is becoming an extremely important means to solve this problem. The core of ATS is to mine the gist of the original text and automatically generate a concise and readable summary. Recently, to better balance and develop these two aspects, deep learning (DL)-based abstractive summarization models have been developed. At present, for ATS tasks, almost all state-of-the-art (SOTA) models are based on DL architecture. However, a comprehensive literature survey is still lacking in the field of DL-based abstractive text summarization. To fill this gap, this paper provides researchers with a comprehensive survey of DL-based abstractive summarization. We first give an overview of abstractive summarization and DL. Then, we summarize several typical frameworks of abstractive summarization. After that, we also give a comparison of several popular datasets that are commonly used for training, validation, and testing. We further analyze the performance of several typical abstractive summarization systems on common datasets. Finally, we highlight some open challenges in the abstractive summarization task and outline some future research trends. We hope that these explorations will provide researchers with new insights into DL-based abstractive summarization.

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Zhang, Xinyuan, Ruiyi Zhang, Manzil Zaheer, and Amr Ahmed. "Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14489–97. http://dx.doi.org/10.1609/aaai.v35i16.17703.

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High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summarization method should consider the multi-speaker scenario where the speakers have different roles, goals, and language styles. In a tete-a-tete, such as a customer-agent conversation, SuTaT aims to summarize for each speaker by modeling the customer utterances and the agent utterances separately while retaining their correlations. SuTaT consists of a conditional generative module and two unsupervised summarization modules. The conditional generative module contains two encoders and two decoders in a variational autoencoder framework where the dependencies between two latent spaces are captured. With the same encoders and decoders, two unsupervised summarization modules equipped with sentence-level self-attention mechanisms generate summaries without using any annotations. Experimental results show that SuTaT is superior on unsupervised dialogue summarization for both automatic and human evaluations, and is capable of dialogue classification and single-turn conversation generation.

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Kapłański, Paweł, Alessandro Seganti, Krzysztof Cieśliński, Aleksandra Chrabrowa, Jerzy Koziolkiewicz, Marcin Bryk, and Iwona Ługowska. "Opening Access To Practice-based Evidence in Clinical Decision Support Systems with Natural Query Language." Annales Universitatis Mariae Curie-Sklodowska, sectio AI – Informatica 16, no. 2 (December 22, 2017): 25. http://dx.doi.org/10.17951/ai.2016.16.2.25.

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<p>Evidence-based medicine can be effective only if constantly tested against errors in medical practice. Clinical record database summarization supported by a machine allows allow to detect anomalies and therefore help detect the errors in early phases of care. Summarization system is a part of Clinical Decision Support Systems however it cannot be used directly by the stakeholder as long as s/he is not able to query the clinical record database. Natural Query Languages allow opening access to data for clinical practitioners, that usually do not have knowledge about articial query languages. Results: We have developed general purpose reporting system called Ask Data Anything (ADA) that we applied to a particular CDSS implementation. As a result, we obtained summarization system that opens the access for these of clinical researchers that were excluded from the meaningful summary of clinical records stored in a given clinical database. The most significant part of the component - NQL parser - is a hybrid of Controlled Natural Language (CNL) and pattern matching with a prior error repair phase. Equipped with reasoning capabilities due to the intensive use of semantic technologies, our hybrid approach allows one to use very simple, keyword-based (even erroneous) queries as well as complex CNL ones with the support of a predictive editor. By using ADA sophisticated summarizations of clinical data are produced as a result of NQL query execution. In this paper, we will present the main ideas underlying ADA component in the context of CDSS.</p>

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Li, Xiaodong, Pangjing Wu, Chenxin Zou, Haoran Xie, and Fu Lee Wang. "Sentiment Lossless Summarization." Knowledge-Based Systems 227 (September 2021): 107170. http://dx.doi.org/10.1016/j.knosys.2021.107170.

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Chettri, Roshna, and Udit Kr. "Automatic Text Summarization." International Journal of Computer Applications 161, no. 1 (March 15, 2017): 5–7. http://dx.doi.org/10.5120/ijca2017912326.

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Otani, Mayu, Yale Song, and Yang Wang. "Video Summarization Overview." Foundations and Trends® in Computer Graphics and Vision 13, no. 4 (2022): 284–335. http://dx.doi.org/10.1561/0600000099.

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Patil, Aarti, Komal Pharande, Dipali Nale, and Roshani Agrawal. "Automatic Text Summarization." International Journal of Computer Applications 109, no. 17 (January 16, 2015): 18–19. http://dx.doi.org/10.5120/19418-0910.

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36

Igarashi, Makoto. "Day 2 Summarization." Acta Oto-Laryngologica 113, sup503 (January 1993): 189–90. http://dx.doi.org/10.3109/00016489309128105.

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Kumar, Amit, and Manoj Kumar Gupta. "Abstractive Summarization System." December 2021 3, no. 4 (April 27, 2022): 309–19. http://dx.doi.org/10.36548/jei.2021.4.006.

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The World Wide Web has evolved into one of the world's most extensive information and knowledge repositories. Despite their ease of access, the great majority of such individual publications are extremely difficult to analyse or evaluate. Text summaries assist users in achieving such information-seeking goals by providing rapid access to the highlights or important features of a document collection. Abstractive summarization attempts to reduce a given text to its core components based on the user's preference for brevity. To summarise, there are two approaches: extraction and abstraction. Statistical techniques are used for extracting most important sentences from a corpus. Abstraction entails reformulating material based on the type of summary. This approach makes use of more adaptive language processing technology. Despite the fact that abstraction yields better summaries, extraction remains the favoured strategy and is widely employed in research. A number of approaches, including cosine, can be used to calculate the measure of resemblance between articles. Sentences' statistical & linguistic features are utilised to determine their importance. An abstractive summary is used to absorb the fundamental concepts of a material and then summarise them into plain English.

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Avellino, Ignacio, Sheida Nozari, Geoffroy Canlorbe, and Yvonne Jansen. "Surgical Video Summarization." Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (April 13, 2021): 1–23. http://dx.doi.org/10.1145/3449214.

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Fule, Suraj. "Video Summarization: Survey." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (May 31, 2019): 2442–45. http://dx.doi.org/10.22214/ijraset.2019.5404.

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SASSI, MINYAR, AMEL GRISSA TOUZI, HABIB OUNELLI, and INES AISSA. "ABOUT DATABASE SUMMARIZATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 02 (April 2010): 133–51. http://dx.doi.org/10.1142/s0218488510006453.

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The summarization system takes a Database (DB) table as input and produces a reduced version of this table through both a rewriting and a generalization process. The resulting table provides records with less precision than the original but it is very informative of the actual DB content. This reduced form can be used as input for advanced Data Mining processes. Several approaches of DB summarization have been proposed in the literature. The most recent is the SaintEtiQ summarization model, proposed initially by Raschia.1 Based on a hierarchical conceptual clustering algorithm, SaintEtiQ builds a summary hierarchy from DB records. In this paper, we propose to extend this DB summarization model by introducing some optimization processes including: (i) minimization of the expert risks domain, (iii) building of the summary hierarchy from DB records, and (iv) cooperation with the user by giving him summaries in different hierarchy levels.

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Murray, Gabriel, Thomas Kleinbauer, Peter Poller, Tilman Becker, Steve Renals, and Jonathan Kilgour. "Extrinsic summarization evaluation." ACM Transactions on Speech and Language Processing 6, no. 2 (October 2009): 1–29. http://dx.doi.org/10.1145/1596517.1596518.

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JOHNSON, E. MARSHALL. "Summarization of Symposium." Toxicological Sciences 5, no. 4 (1985): 653–54. http://dx.doi.org/10.1093/toxsci/5.4.653.

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JOHNSON, E. "Summarization of symposium." Fundamental and Applied Toxicology 5, no. 4 (August 1985): 653–54. http://dx.doi.org/10.1016/0272-0590(85)90188-5.

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Leiva, Luis A. "Responsive text summarization." Information Processing Letters 130 (February 2018): 52–57. http://dx.doi.org/10.1016/j.ipl.2017.10.007.

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Sanada, Shigeru. "A Chairman Summarization." Japanese Journal of Radiological Technology 65, no. 7 (2009): 959. http://dx.doi.org/10.6009/jjrt.65.959.

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Yulianti, Evi, Sharin Huspi, and Mark Sanderson. "Tweet-biased summarization." Journal of the Association for Information Science and Technology 67, no. 6 (April 2, 2015): 1289–300. http://dx.doi.org/10.1002/asi.23496.

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Jawale, Sakshi, Pranit Londhe, Prajwali Kadam, Sarika Jadhav, and Rushikesh Kolekar. "Automatic Text Summarization." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1842–46. http://dx.doi.org/10.22214/ijraset.2023.51815.

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Abstract: Text Summarization is a Natural Language Processing (NLP) method that extracts and collects data from the source and summarizes it. Text summarization has become a requirement for many applications since manually summarizing vast amounts of information is difficult, especially with the expanding magnitude of data. Financial research, search engine optimization, media monitoring, question-answering bots, and document analysis all benefit from text summarization. This paper extensively addresses several summarizing strategies depending on intent, volume of data, and outcome. Our aim is to evaluate and convey an abstract viewpoint of the present scenario research work for text summarization.

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González, José Ángel, Encarna Segarra, Fernando García-Granada, Emilio Sanchis, and Lluís-F. Hurtado. "Attentional Extractive Summarization." Applied Sciences 13, no. 3 (January 22, 2023): 1458. http://dx.doi.org/10.3390/app13031458.

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In this work, a general theoretical framework for extractive summarization is proposed—the Attentional Extractive Summarization framework. Although abstractive approaches are generally used in text summarization today, extractive methods can be especially suitable for some applications, and they can help with other tasks such as Text Classification, Question Answering, and Information Extraction. The proposed approach is based on the interpretation of the attention mechanisms of hierarchical neural networks, which compute document-level representations of documents and summaries from sentence-level representations, which, in turn, are computed from word-level representations. The models proposed under this framework are able to automatically learn relationships among document and summary sentences, without requiring Oracle systems to compute the reference labels for each sentence before the training phase. These relationships are obtained as a result of a binary classification process, the goal of which is to distinguish correct summaries for documents. Two different systems, formalized under the proposed framework, were evaluated on the CNN/DailyMail and the NewsRoom corpora, which are some of the reference corpora in the most relevant works on text summarization. The results obtained during the evaluation support the adequacy of our proposal and suggest that there is still room for the improvement of our attentional framework.

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Kim, Hyun Hee. "Social speech summarization." Proceedings of the American Society for Information Science and Technology 48, no. 1 (2011): 1–4. http://dx.doi.org/10.1002/meet.2011.14504801227.

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Singh, Sandhya, Kevin Patel, Krishnanjan Bhattacharjee, Hemant Darbari, and Seema Verma. "Towards Better Single Document Summarization using Multi-Document Summarization Approach." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 695–703. http://dx.doi.org/10.26438/ijcse/v7i5.695703.

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Journal articles on the topic 'Summarization'

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