Search (114 results, page 3 of 6)

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Abstract

After lying dormant for a few decades, the field of automated text summarization has experienced a tremendous resurgence of interest. Recently, many new algorithms and techniques have been proposed for identifying important information in single documents and document collections, and for mapping this information into grammatical, cohesive, and coherent abstracts. Since 1997, annual workshops, conferences, and large-scale comparative evaluations have provided a rich environment for exchanging ideas between researchers in Asia, Europe, and North America. This entry reviews the main developments in the field and provides a guiding map to those interested in understanding the strengths and weaknesses of an increasingly ubiquitous technology.

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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

The purpose of this study was to develop a method for automatic construction of multidocument summaries of sets of research abstracts that may be retrieved by a digital library or search engine in response to a user query. Sociology dissertation abstracts were selected as the sample domain in this study. A variable-based framework was proposed for integrating and organizing research concepts and relationships as well as research methods and contextual relations extracted from different dissertation abstracts. Based on the framework, a new summarization method was developed, which parses the discourse structure of abstracts, extracts research concepts and relationships, integrates the information across different abstracts, and organizes and presents them in a Web-based interface. The focus of this article is on the user evaluation that was performed to assess the overall quality and usefulness of the summaries. Two types of variable-based summaries generated using the summarization method-with or without the use of a taxonomy-were compared against a sentence-based summary that lists only the research-objective sentences extracted from each abstract and another sentence-based summary generated using the MEAD system that extracts important sentences. The evaluation results indicate that the majority of sociological researchers (70%) and general users (64%) preferred the variable-based summaries generated with the use of the taxonomy.

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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

This paper proposes two approaches to address text summarization: modified corpus-based approach (MCBA) and LSA-based T.R.M. approach (LSA + T.R.M.). The first is a trainable summarizer, which takes into account several features, including position, positive keyword, negative keyword, centrality, and the resemblance to the title, to generate summaries. Two new ideas are exploited: (1) sentence positions are ranked to emphasize the significances of different sentence positions, and (2) the score function is trained by the genetic algorithm (GA) to obtain a suitable combination of feature weights. The second uses latent semantic analysis (LSA) to derive the semantic matrix of a document or a corpus and uses semantic sentence representation to construct a semantic text relationship map. We evaluate LSA + T.R.M. both with single documents and at the corpus level to investigate the competence of LSA in text summarization. The two novel approaches were measured at several compression rates on a data corpus composed of 100 political articles. When the compression rate was 30%, an average f-measure of 49% for MCBA, 52% for MCBA + GA, 44% and 40% for LSA + T.R.M. in single-document and corpus level were achieved respectively.

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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.

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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

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.

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Abstract

General graph random walk has been successfully applied in multi-document summarization, but it has some limitations to process documents by this way. In this paper, we propose a novel hypergraph based vertex-reinforced random walk framework for multi-document summarization. The framework first exploits the Hierarchical Dirichlet Process (HDP) topic model to learn a word-topic probability distribution in sentences. Then the hypergraph is used to capture both cluster relationship based on the word-topic probability distribution and pairwise similarity among sentences. Finally, a time-variant random walk algorithm for hypergraphs is developed to rank sentences which ensures sentence diversity by vertex-reinforcement in summaries. Experimental results on the public available dataset demonstrate the effectiveness of our framework.

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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

Generating summaries that meet the information needs of a user relies on (1) several forms of question decomposition; (2) different summarization approaches; and (3) textual inference for combining the summarization strategies. This novel framework for summarization has the advantage of producing highly responsive summaries, as indicated by the evaluation results.

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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

The UCLA Library reorganized its software developers into focused subteams with one, the Labs Team, dedicated to conducting experiments. In this article we describe our first attempt at conducting a software development experiment, in which we attempted to improve our digital library's search results with metadata from cloud-based image tagging services. We explore the findings and discuss the lessons learned from our first attempt at running an experiment.

Type

a