Search (114 results, page 5 of 6)

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Content

Enthält u.a. Beiträge von: J. BATEMAN u. E. TEICH; R. BRANDOW, K. MITZE u. L.F. RAU; B. ENDRES-NIGGEMEYER, E. MAIER u. A. SIGEL; M.T. MAYBURY; K. McKEOWN, J. ROBIN u. K. KUKICH; A. ROTHKEGEL

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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

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

Sentence clustering plays a pivotal role in theme-based summarization, which discovers topic themes defined as the clusters of highly related sentences to avoid redundancy and cover more diverse information. As the length of sentences is short and the content it contains is limited, the bag-of-words cosine similarity traditionally used for document clustering is no longer suitable. Special treatment for measuring sentence similarity is necessary. In this article, we study the sentence-level clustering problem. After exploiting concept- and context-enriched sentence vector representations, we develop two co-clustering frameworks to enhance sentence-level clustering for theme-based summarization-integrated clustering and interactive clustering-both allowing word and document to play an explicit role in sentence clustering as independent text objects rather than using word or concept as features of a sentence in a document set. In each framework, we experiment with two-level co-clustering (i.e., sentence-word co-clustering or sentence-document co-clustering) and three-level co-clustering (i.e., document-sentence-word co-clustering). Compared against concept- and context-oriented sentence-representation reformation, co-clustering shows a clear advantage in both intrinsic clustering quality evaluation and extrinsic summarization evaluation conducted on the Document Understanding Conferences (DUC) datasets.

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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

We develop a context-based generic cross-lingual retrieval model that can deal with different language pairs. Our model considers contexts in the query translation process. Contexts in the query as weIl as in the documents based an co-occurrence statistics from different granularity of passages are exploited. We also investigate cross-lingual retrieval of automatic generic summaries. We have implemented our model for two different cross-lingual settings, namely, retrieving Chinese documents from English queries as weIl as retrieving English documents from Chinese queries. Extensive experiments have been conducted an a large-scale parallel corpus enabling studies an retrieval performance for two different cross-lingual settings of full-length documents as weIl as automated summaries.

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Abstract

The high quality evaluation of generated summaries is needed if we are to improve automatic summarization systems. Although human evaluation provides better results than automatic evaluation methods, its cost is huge and it is difficult to reproduce the results. Therefore, we need an automatic method that simulates human evaluation if we are to improve our summarization system efficiently. Although automatic evaluation methods have been proposed, they are unreliable when used for individual summaries. To solve this problem, we propose a supervised automatic evaluation method based on a new regression model called the voted regression model (VRM). VRM has two characteristics: (1) model selection based on 'corrected AIC' to avoid multicollinearity, (2) voting by the selected models to alleviate the problem of overfitting. Evaluation results obtained for TSC3 and DUC2004 show that our method achieved error reductions of about 17-51% compared with conventional automatic evaluation methods. Moreover, our method obtained the highest correlation coefficients in several different experiments.

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Abstract

Due to a large variety of noisy information embedded in Web pages, Web-page classification is much more difficult than pure-text classification. In this paper, we propose to improve the Web-page classification performance by removing the noise through summarization techniques. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then put forward a new Web-page summarization algorithm based on Web-page layout and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that the classification algorithms (NB or SVM) augmented by any summarization approach can achieve an improvement by more than 5.0% as compared to pure-text-based classification algorithms. We further introduce an ensemble method to combine the different summarization algorithms. The ensemble summarization method achieves more than 12.0% improvement over pure-text based methods.

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Abstract

The final report of the 2nd British Library abstracting project (the BLAB project), 1990-1992, which was carried out partly at the Computing Department of Lancaster University, and partly at the Centre for Computational Linguistics, UMIST. This project built on the results of the first project, of 1985-1987, to build a system designed create abstracts automatically from given texts

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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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Abstract

The SALOMON project summarises Belgian criminal cases in order to improve access to the large number of existing and future cases. A double methodology was used when developing SALOMON: the cases are processed by employing additional knowledge to interpret structural patterns and features on the one hand and by way of occurrence statistics of index terms on the other. SALOMON performs an initial categorisation and structuring of the cases and subsequently extracts the most relevant text units of the alleged offences and of the opinion of the court. The SALOMON techniques do not themselves solve any legal questions, but they do guide the use effectively towards relevant texts

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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

An evaluation of the Copernic Summarizer, a software for automatically summarizing text in various data formats, is being presented. It shall be assessed if and how the Copernic Summarizer can reasonably be used in the DaimlerChrysler Information Division in order to enhance the quality of its information services. First, an introduction into Automatic Text Summarization is given and the Copernic Summarizer is being presented. Various methods for evaluating Automatic Text Summarization systems and software ergonomics are presented. Two evaluation forms are developed with which the employees of the Information Division shall evaluate the quality and relevance of the extracted keywords and summaries as well as the software's usability. The quality and relevance assessment is done by comparing the original text to the summaries. Finally, a recommendation is given concerning the use of the Copernic Summarizer.

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Abstract

The technological revolution is affecting the structure, form and content of documents, reducing the effectiveness of traditional abstracts that, to some extent, are inadequate to the new documentary conditions. Aims to show the directions in which abstracting/abstracts can evolve to achieve the necessary adequacy in the new digital environments. Three researching trends are proposed: theoretical, methodological and pragmatic. Theoretically, there are some needs for expanding the document concept, reengineering abstracting and designing interdisciplinary models. Methodologically, the trend is toward the structuring, automating and qualifying of the abstracts. Pragmatically, abstracts networking, combined with alternative and complementary models, open a new and promising horizon. Automating, structuring and qualifying abstracting/abstract offer some short-term prospects for progress. Concludes that reengineering, networking and visualising would be middle-term fruitful areas of research toward the full adequacy of abstracting in the new electronic age.

Type

a