Search (95 results, page 2 of 5)

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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

Source

Journal of the American Society for Information Science. 50(1999) no.13, S.1234-1245

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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.

Source

Journal of the Association for Information Science and Technology. 72(2021) no.5, S.583-594

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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

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

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.].

Source

Information processing and management. 43(2007) no.6, S.1571-1587

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Footnote

Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.538-552.

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Footnote

Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.478-483.

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Footnote

Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.534-537.

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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.

Source

Information processing and management. 43(2007) no.6, S.1765-1776

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Abstract

Many automatic text summarization models have been developed in the last decades. Related research in information science has shown that human abstractors extract sentences for summaries based on the hierarchical structure of documents; however, the existing automatic summarization models do not take into account the human abstractor's behavior of sentence extraction and only consider the document as a sequence of sentences during the process of extraction of sentences as a summary. In general, a document exhibits a well-defined hierarchical structure that can be described as fractals - mathematical objects with a high degree of redundancy. In this article, we introduce the fractal summarization model based on the fractal theory. The important information is captured from the source document by exploring the hierarchical structure and salient features of the document. A condensed version of the document that is informatively close to the source document is produced iteratively using the contractive transformation in the fractal theory. The fractal summarization model is the first attempt to apply fractal theory to document summarization. It significantly improves the divergence of information coverage of summary and the precision of summary. User evaluations have been conducted. Results have indicated that fractal summarization is promising and outperforms current summarization techniques that do not consider the hierarchical structure of documents.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.6, S.887-902

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Source

Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried

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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.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.7, S.759-774

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Abstract

We present a new paradigm for the automatic creation of document headlines that is based on direct transformation of relevant textual information into well-formed textual output. Starting from an input document, we automatically create compact representations of weighted finite sets of strings, called WIDL-expressions, which encode the most important topics in the document. A generic natural language generation engine performs the headline generation task, driven by both statistical knowledge encapsulated in WIDL-expressions (representing topic biases induced by the input document) and statistical knowledge encapsulated in language models (representing biases induced by the target language). Our evaluation shows similar performance in quality with a state-of-the-art, extractive approach to headline generation, and significant improvements in quality over previously proposed solutions to abstractive headline generation.

Source

Information processing and management. 43(2007) no.6, S.1536-1548

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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.

Source

Information processing and management. 43(2007) no.6, S.1588-1605

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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

We examined whether the microblog comments given by people after reading a web document could be exploited to improve the accuracy of a web document summarization system. We examined the effect of social information (i.e., tweets) on the accuracy of the generated summaries by comparing the user preference for TBS (tweet-biased summary) with GS (generic summary). The result of crowdsourcing-based evaluation shows that the user preference for TBS was significantly higher than GS. We also took random samples of the documents to see the performance of summaries in a traditional evaluation using ROUGE, which, in general, TBS was also shown to be better than GS. We further analyzed the influence of the number of tweets pointed to a web document on summarization accuracy, finding a positive moderate correlation between the number of tweets pointed to a web document and the performance of generated TBS as measured by user preference. The results show that incorporating social information into the summary generation process can improve the accuracy of summary. The reason for people choosing one summary over another in a crowdsourcing-based evaluation is also presented in this article.

Source

Journal of the Association for Information Science and Technology. 67(2016) no.6, S.1289-1300

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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

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.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.2, S.234-248

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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

Source

Journal of the American Society for Information Science. 51(2000) no.8, S.745-756