Search (17 results, page 1 of 1)

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Abstract

Presents a system for summarizing quantitative data in natural language, focusing on the use of a corpus of basketball game summaries, drawn from online news services, to empirically shape the system design and to evaluate the approach. Initial corpus analysis revealed characteristics of textual summaries that challenge the capabilities of current language generation systems. A revision based corpus analysis was used to identify and encode the revision rules of the system. Presents a quantitative evaluation, using several test corpora, to measure the robustness of the new revision based model

Date

6. 3.1997 16:22:15

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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

Selective Analysis is a new method for text summarization of technical articles whose design is based on the study of a corpus of professional abstracts and technical documents The method emphasizes the selection of particular types of information and its elaboration exploring the issue of dynamical summarization. A computer prototype was developed to demonstrate the viability of the approach and the automatic abstracts were evaluated using human informants. The results so far obtained indicate that the summaries are acceptable in content and text quality

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Abstract

Browsing a database of article abstracts is one way to select and buy relevant magazine articles online. Our research contributes to the design and development of text grammars for abstracting texts in unlimited subject domains. We developed a system that parses texts based on the text grammar of a specific text type and that extracts sentences and statements which are relevant for inclusion in the abstracts. The system employs knowledge of the discourse patterns that are typical of news stories. The results are encouraging and demonstrate the importance of discourse structures in text summarisation.

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Date

26. 2.1997 10:22:43

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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

Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel integrated information retrieval system-the Query, Cluster, Summarize (QCS) system-which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of methods in the QCS design improves retrievals by providing users more focused information organized by topic. We demonstrate the improved performance by a series of experiments using standard test sets from the Document Understanding Conferences (DUC) as measured by the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines. Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence "trimming" and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format. Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design, and the value of this particular combination of modules.

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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

Describes the design and implementation of a text extraction tool, NEWS_EXT, which aztomatically produces summaries from news reports by extracting sentences to form indicative abstracts. Selection of sentences is based on sentence importance, measured by means of sentence scoring or simple linguistic analysis of sentence structure. Tests were conducted on 4 approaches for the functioning of the NEWS_EXT system; extraction by keyword frequency; extraction by title keywords; extraction by location; and extraction by indicative phrase. Reports results of a study to compare the results of the application of NEWS_EXT with manually produced extracts; using relevance as the criterion for effectiveness. 48 newspaper articles were assessed (The Straits Times, International Herald Tribune, Asian Wall Street Journal, and Financial Times). The evaluation was conducted in 2 stages: stage 1 involving abstracts produced manually by 2 human experts; stage 2 involving the generation of abstracts using NEWS_EXT. Results of each of the 4 approaches were compared with the human produced abstracts, where the title and location approaches were found to give the best results for both local and foreign news. Reports plans to refine and enhance NEWS_EXT and incorporate it as a module within a larger newspaper clipping system

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Abstract

Purpose - The purpose of this research is to develop a method for automatic construction of multi-document summaries of sets of news articles that might be retrieved by a web search engine in response to a user query. Design/methodology/approach - Based on the cross-document discourse analysis, an event-based framework is proposed for integrating and organizing information extracted from different news articles. It has a hierarchical structure in which the summarized information is presented at the top level and more detailed information given at the lower levels. A tree-view interface was implemented for displaying a multi-document summary based on the framework. A preliminary user evaluation was performed by comparing the framework-based summaries against the sentence-based summaries. Findings - In a small evaluation, all the human subjects preferred the framework-based summaries to the sentence-based summaries. It indicates that the event-based framework is an effective way to summarize a set of news articles reporting an event or a series of relevant events. Research limitations/implications - Limited to event-based news articles only, not applicable to news critiques and other kinds of news articles. A summarization system based on the event-based framework is being implemented. Practical implications - Multi-document summarization of news articles can adopt the proposed event-based framework. Originality/value - An event-based framework for summarizing sets of news articles was developed and evaluated using a tree-view interface for displaying such summaries.

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Abstract

Sentence ranking is the issue of most concern in document summarization today. While traditional feature-based approaches evaluate sentence significance and rank the sentences relying on the features that are particularly designed to characterize the different aspects of the individual sentences, the newly emerging graph-based ranking algorithms (such as the PageRank-like algorithms) recursively compute sentence significance using the global information in a text graph that links sentences together. In general, the existing PageRank-like algorithms can model well the phenomena that a sentence is important if it is linked by many other important sentences. Or they are capable of modeling the mutual reinforcement among the sentences in the text graph. However, when dealing with multidocument summarization these algorithms often assemble a set of documents into one large file. The document dimension is totally ignored. In this article we present a framework to model the two-level mutual reinforcement among sentences as well as documents. Under this framework we design and develop a novel ranking algorithm such that the document reinforcement is taken into account in the process of sentence ranking. The convergence issue is examined. We also explore an interesting and important property of the proposed algorithm. When evaluated on the DUC 2005 and 2006 query-oriented multidocument summarization datasets, significant results are achieved.

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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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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