Search (57 results, page 1 of 3)

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Abstract

With the onset of the information explosion arising from digital libraries and access to a wealth of information through the Internet, the need to efficiently determine the relevance of a document becomes even more urgent. Describes a text extraction system (TES), which retrieves a set of sentences from a document to form an indicative abstract. Such an automated process enables information to be filtered more quickly. Discusses the combination of various text extraction techniques. Compares results with manually produced abstracts

Date

26. 2.1997 10:22:43

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Abstract

Applies the ideas from the automatic link generation research to automatic text summarisation. Using techniques for inter-document link generation, generates intra-document links between passages of a document. Based on the intra-document linkage pattern of a text, characterises the structure of the text. Applies the knowledge of text structure to do automatic text summarisation by passage extraction. Evaluates a set of 50 summaries generated using these techniques by comparing the to paragraph extracts constructed by humans. The automatic summarisation methods perform well, especially in view of the fact that the summaries generates by 2 humans for the same article are surprisingly dissimilar

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Abstract

The volume of entity-centric structured data grows rapidly on the Web. The description of an entity, composed of property-value pairs (a.k.a. features), has become very large in many applications. To avoid information overload, efforts have been made to automatically select a limited number of features to be shown to the user based on certain criteria, which is called automatic entity summarization. However, to the best of our knowledge, there is a lack of extensive studies on how humans rank and select features in practice, which can provide empirical support and inspire future research. In this article, we present a large-scale statistical analysis of the descriptions of entities provided by DBpedia and the abstracts of their corresponding Wikipedia articles, to empirically study, along several different dimensions, which kinds of features are preferable when humans summarize. Implications for automatic entity summarization are drawn from the findings.

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Abstract

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.

Date

22. 6.2023 14:55:20

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Abstract

In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.

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Abstract

We propose a tag-based framework that simulates human abstractors' ability to select significant sentences based on key concepts in a sentence as well as the semantic relations between key concepts to create generic summaries of transcribed lecture videos. The proposed extractive summarization method uses tags (viewer- and author-assigned terms) as key concepts. Our method employs Flickr tag clusters and WordNet synonyms to expand tags and detect the semantic relations between tags. This method helps select sentences that have a greater number of semantically related key concepts. To investigate the effectiveness and uniqueness of the proposed method, we compare it with an existing technique, latent semantic analysis (LSA), using intrinsic and extrinsic evaluations. The results of intrinsic evaluation show that the tag-based method is as or more effective than the LSA method. We also observe that in the extrinsic evaluation, the grand mean accuracy score of the tag-based method is higher than that of the LSA method, with a statistically significant difference. Elaborating on our results, we discuss the theoretical and practical implications of our findings for speech video summarization and retrieval.

Date

22. 1.2016 12:29:41

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Abstract

The automatic summarization of scientific articles differs from other text genres because of the structured format and longer text length. Previous approaches have focused on tackling the lengthy nature of scientific articles, aiming to improve the computational efficiency of summarizing long text using a flat, unstructured abstract. However, the structured format of scientific articles and characteristics of each section have not been fully explored, despite their importance. The lack of a sufficient investigation and discussion of various characteristics for each section and their influence on summarization results has hindered the practical use of automatic summarization for scientific articles. To provide a balanced abstract proportionally emphasizing each section of a scientific article, the community introduced the structured abstract, an abstract with distinct, labeled sections. Using this information, in this study, we aim to understand tasks ranging from data preparation to model evaluation from diverse viewpoints. Specifically, we provide a preprocessed large-scale dataset and propose a summarization method applying the introduction, methods, results, and discussion (IMRaD) format reflecting the characteristics of each section. We also discuss the objective benchmarks and perspectives of state-of-the-art algorithms and present the challenges and research directions in this area.

Date

22. 1.2023 18:57:12

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Abstract

Recent years have seen increased interest in text summarization with emphasis on evaluation of prototype systems. Many factors can affect the design of such evaluations, requiring choices among competing alternatives. This paper examines several major themes running through three evaluations: SUMMAC, NTCIR, and DUC, with a concentration on DUC. The themes are extrinsic and intrinsic evaluation, evaluation procedures and methods, generic versus focused summaries, single- and multi-document summaries, length and compression issues, extracts versus abstracts, and issues with genre.

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Abstract

The UCLA Library reorganized its software developers into focused subteams with one, the Labs Team, dedicated to conducting experiments. In this article we describe our first attempt at conducting a software development experiment, in which we attempted to improve our digital library's search results with metadata from cloud-based image tagging services. We explore the findings and discuss the lessons learned from our first attempt at running an experiment.

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Abstract

Describes computerized tools for computer assisted abstracting. FlipPhr is a Microsoft Windows application program that rearranges (flips) phrases or other expressions in accordance with rules in a grammar. The flipping may be invoked with a single keystroke from within various Windows application programs that allow cutting and pasting of text. The user may modify the grammar to provide for different kinds of flipping

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Abstract

This paper introduces a hybrid method for abstracting Chinese text. It integrates the statistical approach with language understanding. Some linguistics heuristics and segmentation are also incorporated into the abstracting process. The prototype system is of a multipurpose type catering for various users with different reqirements. Initial responses show that the proposed method contributes much to the flexibility and accuracy of the automatic Chinese abstracting system. In practice, the present work provides a path to developing an intelligent Chinese system for automating the information

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Abstract

The paper focuses on a particular approach to automatic sentence compression which makes use of a discriminative sequence classifier known as Conditional Random Fields (CRF). We devise several features for CRF that allow it to incorporate information on nonlinear relations among words. Along with that, we address the issue of data paucity by collecting data from RSS feeds available on the Internet, and turning them into training data for use with CRF, drawing on techniques from biology and information retrieval. We also discuss a recursive application of CRF on the syntactic structure of a sentence as a way of improving the readability of the compression it generates. Experiments found that our approach works reasonably well compared to the state-of-the-art system [Knight, K., & Marcu, D. (2002). Summarization beyond sentence extraction: A probabilistic approach to sentence compression. Artificial Intelligence 139, 91-107.].

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Abstract

Access to the vast body of research literature that is now available on biomedicine and related fields can be improved with automatic summarization. This paper describes a summarization system for the biomedical domain that represents documents as graphs formed from concepts and relations in the UMLS Metathesaurus. This system has to deal with the ambiguities that occur in biomedical documents. We describe a variety of strategies that make use of MetaMap and Word Sense Disambiguation (WSD) to accurately map biomedical documents onto UMLS Metathesaurus concepts. Evaluation is carried out using a collection of 150 biomedical scientific articles from the BioMed Central corpus. We find that using WSD improves the quality of the summaries generated.

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Abstract

Sophisticated documents like legal cases and biomedical articles can contain unusually long sentences. Extractive summarizers can select such sentences-potentially adding hundreds of unnecessary words to the summary-or exclude them and lose important content. Sentence simplification or compression seems on the surface to be a promising solution. However, compression removes words before the selection algorithm can use them, and simplification generates sentences that may be ambiguous in an extractive summary. We therefore compare the performance of an extractive summarizer selecting from the sentences of the original document with that of the summarizer selecting from sentences shortened in three ways: simplification, compression, and disaggregation, which splits one sentence into several according to rules designed to keep all meaning. We find that on legal cases and biomedical articles, these shortening methods generate ungrammatical output. Human evaluators performed an extrinsic evaluation consisting of comprehension questions about the summaries. Evaluators given compressed, simplified, or disaggregated versions of the summaries answered fewer questions correctly than did those given summaries with unaltered sentences. Error analysis suggests 2 causes: Altered sentences sometimes interact with the sentence selection algorithm, and alterations to sentences sometimes obscure information in the summary. We discuss future work to alleviate these problems.

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Abstract

Experimental subjects wrote abstracts of an article using a simplified version of the TEXNET abstracting assistance software. In addition to the fulltext, the 35 subjects were presented with either keywords or phrases extracted automatically. The resulting abstracts, and the times taken, were recorded automatically; some additional information was gathered by oral questionnaire. Results showed considerable variation among subjects, but 37% found the keywords or phrases quite or very useful in writing their abstracts. Statistical analysis failed to support deveral hypothesised relations; phrases were not viewed as significantly more helpful than keywords; and abstracting experience did not correlate with originality of wording, approximation of the author abstract, or greater conciseness. Results also suggested possible modifications to the software

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Abstract

Experimental subjects wrote abstracts using a simplified version of the TEXNET abstracting assistance software. In addition to the full text, subjects were presented with either keywords or phrases extracted automatically. The resulting abstracts, and the times taken, were recorded automatically; some additional information was gathered by oral questionnaire. Selected abstracts produced were evaluated on various criteria by independent raters. Results showed considerable variation among subjects, but 37% found the keywords or phrases 'quite' or 'very' useful in writing their abstracts. Statistical analysis failed to support several hypothesized relations: phrases were not viewed as significantly more helpful than keywords; and abstracting experience did not correlate with originality of wording, approximation of the author abstract, or greater conciseness. Requiring further study are some unanticipated strong correlations including the following: Windows experience and writing an abstract like the author's; experience reading abstracts and thinking one had written a good abstract; gender and abstract length; gender and use of words and phrases from the original text. Results have also suggested possible modifications to the TEXNET software

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Abstract

Summarizing is the process of reducing the large information size of something like a novel or a scientific paper to a short summary or abstract comprising only the most essential points. Summarizing is frequent in everyday communication, but it is also a professional skill for journalists and others. Automated summarizing functions are urgently needed by Internet users who wish to avoid being overwhelmed by information. This book presents the state of the art and surveys related research; it deals with everyday and professional summarizing as well as computerized approaches. The author focuses in detail on the cognitive pro-cess involved in summarizing and supports this with a multimedia simulation systems on the accompanying CD-ROM

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Abstract

This paper reviews research on automatic summarising in the last decade. This work has grown, stimulated by technology and by evaluation programmes. The paper uses several frameworks to organise the review, for summarising itself, for the factors affecting summarising, for systems, and for evaluation. The review examines the evaluation strategies applied to summarising, the issues they raise, and the major programmes. It considers the input, purpose and output factors investigated in recent summarising research, and discusses the classes of strategy, extractive and non-extractive, that have been explored, illustrating the range of systems built. The conclusions drawn are that automatic summarisation has made valuable progress, with useful applications, better evaluation, and more task understanding. But summarising systems are still poorly motivated in relation to the factors affecting them, and evaluation needs taking much further to engage with the purposes summaries are intended to serve and the contexts in which they are used.

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Abstract

This article introduces a new task-based evaluation measure called Relevance Prediction that is a more intuitive measure of an individual's performance on a real-world task than interannotator agreement. Relevance Prediction parallels what a user does in the real world task of browsing a set of documents using standard search tools, i.e., the user judges relevance based on a short summary and then that same user - not an independent user - decides whether to open (and judge) the corresponding document. This measure is shown to be a more reliable measure of task performance than LDC Agreement, a current gold-standard based measure used in the summarization evaluation community. Our goal is to provide a stable framework within which developers of new automatic measures may make stronger statistical statements about the effectiveness of their measures in predicting summary usefulness. We demonstrate - as a proof-of-concept methodology for automatic metric developers - that a current automatic evaluation measure has a better correlation with Relevance Prediction than with LDC Agreement and that the significance level for detected differences is higher for the former than for the latter.

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Abstract

We argue that the quality of a summary can be evaluated based on how many concepts in the original document(s) that can be preserved after summarization. Here, a concept refers to an abstract or concrete entity or its action often expressed by diverse terms in text. Summary generation can thus be considered as an optimization problem of selecting a set of sentences with minimal answer loss. In this paper, we propose a document concept lattice that indexes the hierarchy of local topics tied to a set of frequent concepts and the corresponding sentences containing these topics. The local topics will specify the promising sub-spaces related to the selected concepts and sentences. Based on this lattice, the summary is an optimized selection of a set of distinct and salient local topics that lead to maximal coverage of concepts with the given number of sentences. Our summarizer based on the concept lattice has demonstrated competitive performance in Document Understanding Conference 2005 and 2006 evaluations as well as follow-on tests.