Search (57 results, page 2 of 3)

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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

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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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

Sentiment analysis concerns the study of opinions expressed in a text. This paper presents the QMOS method, which employs a combination of sentiment analysis and summarization approaches. It is a lexicon-based method to query-based multi-documents summarization of opinion expressed in reviews. QMOS combines multiple sentiment dictionaries to improve word coverage limit of the individual lexicon. A major problem for a dictionary-based approach is the semantic gap between the prior polarity of a word presented by a lexicon and the word polarity in a specific context. This is due to the fact that, the polarity of a word depends on the context in which it is being used. Furthermore, the type of a sentence can also affect the performance of a sentiment analysis approach. Therefore, to tackle the aforementioned challenges, QMOS integrates multiple strategies to adjust word prior sentiment orientation while also considers the type of sentence. QMOS also employs the Semantic Sentiment Approach to determine the sentiment score of a word if it is not included in a sentiment lexicon. On the other hand, the most of the existing methods fail to distinguish the meaning of a review sentence and user's query when both of them share the similar bag-of-words; hence there is often a conflict between the extracted opinionated sentences and users' needs. However, the summarization phase of QMOS is able to avoid extracting a review sentence whose similarity with the user's query is high but whose meaning is different. The method also employs the greedy algorithm and query expansion approach to reduce redundancy and bridge the lexical gaps for similar contexts that are expressed using different wording, respectively. Our experiment shows that the QMOS method can significantly improve the performance and make QMOS comparable to other existing methods.

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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

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

Our aim was to develop an event-related potential (ERP)-based method to construct a video skim consisting of key shots to bridge the semantic gap between the topic inferred from a whole video and that from its summary. Mayer's cognitive model was examined, wherein the topic integration process of a user evoked by a visual stimulus can be associated with long-latency ERP components. We determined that long-latency ERP components are suitable for measuring a user's neuronal response through a literature review. We hypothesized that N300 is specific to the categorization of all shots regardless of topic relevance, N400 is specific for the semantic mismatching process for topic-irrelevant shots, and P600 is specific for the context updating process for topic-relevant shots. In our experiment, the N400 component led to more negative ERP signals in response to topic-irrelevant shots than to topic-relevant shots and showed a fronto-central scalp pattern. P600 elicited more positive ERP signals for topic-relevant shots than for topic-irrelevant shots and showed a fronto-central scalp pattern. We used discriminant and artificial neural network (ANN) analyses to decode video shot relevance and observed that the ANN produced particularly high success rates: 91.3% from the training set and 100% from the test set.

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Abstract

We propose a new method for using anaphoric information in Latent Semantic Analysis (lsa), and discuss its application to develop an lsa-based summarizer which achieves a significantly better performance than a system not using anaphoric information, and a better performance by the rouge measure than all but one of the single-document summarizers participating in DUC-2002. Anaphoric information is automatically extracted using a new release of our own anaphora resolution system, guitar, which incorporates proper noun resolution. Our summarizer also includes a new approach for automatically identifying the dimensionality reduction of a document on the basis of the desired summarization percentage. Anaphoric information is also used to check the coherence of the summary produced by our summarizer, by a reference checker module which identifies anaphoric resolution errors caused by sentence extraction.

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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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Date

22. 1.2016 12:29:41

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Date

27.11.2005 18:29:55

Source

Journal of information science. 29(2003) no.5, S.405-418

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Abstract

Describes the abstracting assistance features being prototyped in the TEXNET text network management system. Sentence weighting methods include: weithing negatively or positively on the stems in a selected passage; weighting on general lists of cue words, adjusting weights of selected segments; and weighting of occurrence of frequent stems. The user may adjust a number of parameters: the minimum strength of extracts; the threshold for frequent word/stems and the amount sentence weight is to be adjusted for each weighting type

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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

Develops a cognitive model of expert summarization, using 54 working processes of 6 experts recorded by thinking-alound protocols. It comprises up to 140 working steps. Components of the model are a toolbox of empirically founded strategies, principles of process organization, and interpreted working steps where the interaction of cognitive strategies can be investigated. In the computerized simulation the SimSum (Simulation of Summarizing) system, cognitive strategies are represented by object-oriented agents grouped around dedicated blckboards

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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

4 working steps taken from a comprehensive empirical model of expert abstracting are studied in order to prepare an explorative implementation of a simulation model. It aims at explaining the knowledge processing activities during professional summarizing. Following the case-based and holistic strategy of qualitative empirical research, the main features of the simulation system were developed by investigating in detail a small but central test case - 4 working steps where an expert abstractor discovers what the paper is about and drafts the topic sentence of the abstract

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Abstract

Summarization entails analysis of source material, selection of key information, condensation of this, and generation of a compct summary form. While there habe been many investigations into the automatic summarization of text, relatively little attention has been given to the summarization of information from structured information sources such as data of knowledge bases, despite this being a desirable capability for a number of application areas including report generation from databases (e.g. weather, financial, medical) and simulation (e.g. military, manufacturing, aconomic). After a brief introduction indicating the main elements of summarization and referring to some illustrative approaches to it, considers pecific issues in the generation of text summaries of event data, describes a system, SumGen, which selects key information from an event database by reasoning about event frequencies, frequencies of relations between events, and domain specific importance measures. Describes how Sum Gen then aggregates similar information and plans a summary presentations tailored to stereotypical users

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Abstract

The SALOMON system automatically summarizes Belgian criminal cases in order to improve access to the large number of existing and future court decisions. SALOMON extracts relevant text units from the case text to form a case summary. Such a case profile facilitates the rapid determination of the relevance of the case or may be employed in text search. In a first important abstracting step SALOMON performs an initial categorization of legal criminal cases and structures the case text into separate legally relevant and irrelevant components. A text grammar represented as a semantic network is used to automatically determine the category of the case and its components. Extracts from the case general data and identifies text portions relevant for further abstracting. Prior knowledge of the text structure and its indicative cues may support automatic abstracting. A text grammar is a promising form for representing the knowledge involved