Search (20 results, page 1 of 1)

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Abstract

Automatic summarizing is a research topic whose time has come. The papers illustrate some of the relevant work already under way. Places these papers in their wider context: why research and development on automatic summarizing is timely, what areas of work and ideas it should draw on, how future investigations and experiments can be effectively framed

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Abstract

The work in automatic text summary focuses mainly on computational models of texts. The artificial intelligence related work in text summary deals mainly with narrative texts such as newspaper reports and stories. Presents a study on the summary of non-narrative texts such as those in scientific and technical communication. Discusses syntactic cohesion; lexical cohesion; complex lexical repetition; simple and complex paraphrase; bonds and links; and Tele-pattan; an architecture for cohesion based text analysis and summarisation system working on SGML

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Abstract

Presents a method for the automated analysis and comprehension of foreign affairs news produced by a Chinese news agency. Notes that the development of the method was prededed by a study of the structuring rules of the news. Describes how an abstract of the news story is produced automatically from the analysis. Stresses the main aim of the work which is to use specch act theory to analyse and classify sentences

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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 work described here initially formed part of a triangulation exercise to establish the effectiveness of the Query Term Order algorithm. It subsequently proved to be a reliable indicator for summarising English web documents. We utilised the human summaries from the Document Understanding Conference data, and generated queries automatically for testing the QTO algorithm. Six sentence weighting schemes that made use of Query Term Frequency and QTO were constructed to produce system summaries, and this paper explains the process of combining and balancing the weighting components. The summaries produced were evaluated by the ROUGE-1 metric, and the results showed that using QTO in a weighting combination resulted in the best performance. We also found that using a combination of more weighting components always produced improved performance compared to any single weighting component.

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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 paper presents a model that incorporates contemporary theories of tense and aspect and develops a new framework for extracting temporal relations between two sentence-internal events, given their tense, aspect, and a temporal connecting word relating the two events. A linguistic constraint on event combination has been implemented to detect incorrect parser analyses and potentially apply syntactic reanalysis or semantic reinterpretation - in preparation for subsequent processing for multi-document summarization. An important contribution of this work is the extension of two different existing theoretical frameworks - Hornstein's 1990 theory of tense analysis and Allen's 1984 theory on event ordering - and the combination of both into a unified system for representing and constraining combinations of different event types (points, closed intervals, and open-ended intervals). We show that our theoretical results have been verified in a large-scale corpus analysis. The framework is designed to inform a temporally motivated sentence-ordering module in an implemented multi-document summarization system.

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Abstract

Most knowledge accumulated through scientific discoveries in genomics and related biomedical disciplines is buried in the vast amount of biomedical literature. Since understanding gene regulations is fundamental to biomedical research, summarizing all the existing knowledge about a gene based on literature is highly desirable to help biologists digest the literature. In this paper, we present a study of methods for automatically generating gene summaries from biomedical literature. Unlike most existing work on automatic text summarization, in which the generated summary is often a list of extracted sentences, we propose to generate a semi-structured summary which consists of sentences covering specific semantic aspects of a gene. Such a semi-structured summary is more appropriate for describing genes and poses special challenges for automatic text summarization. We propose a two-stage approach to generate such a summary for a given gene - first retrieving articles about a gene and then extracting sentences for each specified semantic aspect. We address the issue of gene name variation in the first stage and propose several different methods for sentence extraction in the second stage. We evaluate the proposed methods using a test set with 20 genes. Experiment results show that the proposed methods can generate useful semi-structured gene summaries automatically from biomedical literature, and our proposed methods outperform general purpose summarization methods. Among all the proposed methods for sentence extraction, a probabilistic language modeling approach that models gene context performs the best.

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Abstract

Text summarization is a method for data reduction. The use of text summarization enables users to reduce the amount of text that must be read while still assimilating the core information. The data reduction offered by text summarization is particularly useful in the biomedical domain, where physicians must continuously find clinical trial study information to incorporate into their patient treatment efforts. Such efforts are often hampered by the high-volume of publications. This paper presents two independent methods (BioChain and FreqDist) for identifying salient sentences in biomedical texts using concepts derived from domain-specific resources. Our semantic-based method (BioChain) is effective at identifying thematic sentences, while our frequency-distribution method (FreqDist) removes information redundancy. The two methods are then combined to form a hybrid method (ChainFreq). An evaluation of each method is performed using the ROUGE system to compare system-generated summaries against a set of manually-generated summaries. The BioChain and FreqDist methods outperform some common summarization systems, while the ChainFreq method improves upon the base approaches. Our work shows that the best performance is achieved when the two methods are combined. The paper also presents a brief physician's evaluation of three randomly-selected papers from an evaluation corpus to show that the author's abstract does not always reflect the entire contents of the full-text.

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Abstract

Purpose - Scientific abstracts reproduce only part of the information and the complexity of argumentation in a scientific article. The purpose of this paper provides a first analysis of the similarity between the text of scientific abstracts and the body of articles, using sentences as the basic textual unit. It contributes to the understanding of the structure of abstracts. Design/methodology/approach - Using sentence-based similarity metrics, the authors quantify the phenomenon of text re-use in abstracts and examine the positions of the sentences that are similar to sentences in abstracts in the introduction, methods, results and discussion structure, using a corpus of over 85,000 research articles published in the seven Public Library of Science journals. Findings - The authors provide evidence that 84 percent of abstract have at least one sentence in common with the body of the paper. Studying the distributions of sentences in the body of the articles that are re-used in abstracts, the authors show that there exists a strong relation between the rhetorical structure of articles and the zones that authors re-use when writing abstracts, with sentences mainly coming from the beginning of the introduction and the end of the conclusion. Originality/value - Scientific abstracts contain what is considered by the author(s) as information that best describe documents' content. This is a first study that examines the relation between the contents of abstracts and the rhetorical structure of scientific articles. The work might provide new insight for improving automatic abstracting tools as well as information retrieval approaches, in which text organization and structure are important features.

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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

Online reputation management (ORM) comprises the collection of techniques that help monitoring and improving the public image of an entity (companies, products, institutions) on the Internet. The ORM experts try to minimize the negative impact of the information about an entity while maximizing the positive material for being more trustworthy to the customers. Due to the huge amount of information that is published on the Internet every day, there is a need to summarize the entire flow of information to obtain only those data that are relevant to the entities. Traditionally the automatic summarization task in the ORM scenario takes some in-domain signals into account such as popularity, polarity for reputation and novelty but exists other feature to be considered, the authority of the people. This authority depends on the ability to convince others and therefore to influence opinions. In this work, we propose the use of authority signals that measures the influence of a user jointly with (a) priority signals related to the ORM domain and (b) information regarding the different topics that influential people is talking about. Our results indicate that the use of authority signals may significantly improve the quality of the summaries that are automatically generated.

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Date

26. 2.1997 10:22:43

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Date

6. 3.1997 16:22:15

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Source

Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon

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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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Date

22. 7.2006 17:25:48

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Date

22. 1.2016 12:29:41

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Date

22. 1.2023 18:57:12

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Date

22. 6.2023 14:55:20