Search (114 results, page 1 of 6)

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Abstract

With the onset of the information explosion arising from digital libraries and access to a wealth of information through the Internet, the need to efficiently determine the relevance of a document becomes even more urgent. Describes a text extraction system (TES), which retrieves a set of sentences from a document to form an indicative abstract. Such an automated process enables information to be filtered more quickly. Discusses the combination of various text extraction techniques. Compares results with manually produced abstracts

Date

26. 2.1997 10:22:43

Type

a

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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

Type

a

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Abstract

Describes the application of weighting strategies to model uncertainties and probabilities in automatic abstracting systems, particularly in the concept selection phase. The weights were originally assigned in an ad hoc manner and were then refined by manual analysis of the results. The new method attempts to derive a more systematic methods and performs this using a genetic algorithm

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

Type

a

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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.

Type

a

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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.

Type

a

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Abstract

Document keyphrases provide a concise summary of a document's content, offering semantic metadata summarizing a document. They can be used in many applications related to knowledge management and text mining, such as automatic text summarization, development of search engines, document clustering, document classification, thesaurus construction, and browsing interfaces. Because only a small portion of documents have keyphrases assigned by authors, and it is time-consuming and costly to manually assign keyphrases to documents, it is necessary to develop an algorithm to automatically generate keyphrases for documents. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified phrases to assign weights to the candidate keyphrases. The logic of our algorithm is: The more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. KIP's learning function can enrich the glossary database by automatically adding new identified keyphrases to the database. KIP's personalization feature will let the user build a glossary database specifically suitable for the area of his/her interest. The evaluation results show that KIP's performance is better than the systems we compared to and that the learning function is effective.

Date

22. 7.2006 17:25:48

Type

a

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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.

Date

22. 1.2023 18:57:12

Type

a

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Abstract

We propose a tag-based framework that simulates human abstractors' ability to select significant sentences based on key concepts in a sentence as well as the semantic relations between key concepts to create generic summaries of transcribed lecture videos. The proposed extractive summarization method uses tags (viewer- and author-assigned terms) as key concepts. Our method employs Flickr tag clusters and WordNet synonyms to expand tags and detect the semantic relations between tags. This method helps select sentences that have a greater number of semantically related key concepts. To investigate the effectiveness and uniqueness of the proposed method, we compare it with an existing technique, latent semantic analysis (LSA), using intrinsic and extrinsic evaluations. The results of intrinsic evaluation show that the tag-based method is as or more effective than the LSA method. We also observe that in the extrinsic evaluation, the grand mean accuracy score of the tag-based method is higher than that of the LSA method, with a statistically significant difference. Elaborating on our results, we discuss the theoretical and practical implications of our findings for speech video summarization and retrieval.

Date

22. 1.2016 12:29:41

Type

a

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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.

Date

22. 6.2023 14:55:20

Type

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Abstract

This article introduces a new task-based evaluation measure called Relevance Prediction that is a more intuitive measure of an individual's performance on a real-world task than interannotator agreement. Relevance Prediction parallels what a user does in the real world task of browsing a set of documents using standard search tools, i.e., the user judges relevance based on a short summary and then that same user - not an independent user - decides whether to open (and judge) the corresponding document. This measure is shown to be a more reliable measure of task performance than LDC Agreement, a current gold-standard based measure used in the summarization evaluation community. Our goal is to provide a stable framework within which developers of new automatic measures may make stronger statistical statements about the effectiveness of their measures in predicting summary usefulness. We demonstrate - as a proof-of-concept methodology for automatic metric developers - that a current automatic evaluation measure has a better correlation with Relevance Prediction than with LDC Agreement and that the significance level for detected differences is higher for the former than for the latter.

Type

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Abstract

The potential of summary personalization is high, because a summary that would be useless to decide the relevance of a document if summarized in a generic manner, may be useful if the right sentences are selected that match the user interest. In this paper we defend the use of a personalized summarization facility to maximize the density of relevance of selections sent by a personalized information system to a given user. The personalization is applied to the digital newspaper domain and it used a user-model that stores long and short term interests using four reference systems: sections, categories, keywords and feedback terms. On the other side, it is crucial to measure how much information is lost during the summarization process, and how this information loss may affect the ability of the user to judge the relevance of a given document. The results obtained in two personalization systems show that personalized summaries perform better than generic and generic-personalized summaries in terms of identifying documents that satisfy user preferences. We also considered a user-centred direct evaluation that showed a high level of user satisfaction with the summaries.

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Abstract

Summary writing is a process for creating a short version of a source text. It can be used as a measure of understanding. As grading students' summaries is a very time-consuming task, computer-assisted assessment can help teachers perform the grading more effectively. Several techniques, such as BLEU, ROUGE, N-gram co-occurrence, Latent Semantic Analysis (LSA), LSA_Ngram and LSA_ERB, have been proposed to support the automatic assessment of students' summaries. Since these techniques are more suitable for long texts, their performance is not satisfactory for the evaluation of short summaries. This paper proposes a specialized method that works well in assessing short summaries. Our proposed method integrates the semantic relations between words, and their syntactic composition. As a result, the proposed method is able to obtain high accuracy and improve the performance compared with the current techniques. Experiments have displayed that it is to be preferred over the existing techniques. A summary evaluation system based on the proposed method has also been developed.

Type

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Imprint

Potsdam : Fachhochschule, FB A-B-D

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Abstract

Keywords can be considered as condensed versions of documents and short forms of their summaries. In this paper, the problem of automatic extraction of keywords from documents is treated as a supervised learning task. A lexical chain holds a set of semantically related words of a text and it can be said that a lexical chain represents the semantic content of a portion of the text. Although lexical chains have been extensively used in text summarization, their usage for keyword extraction problem has not been fully investigated. In this paper, a keyword extraction technique that uses lexical chains is described, and encouraging results are obtained.

Type

a

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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.

Type

a

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Abstract

This article examines the application of two single-document sentence compression techniques to the problem of multi-document summarization-a "parse-and-trim" approach and a statistical noisy-channel approach. We introduce the multi-candidate reduction (MCR) framework for multi-document summarization, in which many compressed candidates are generated for each source sentence. These candidates are then selected for inclusion in the final summary based on a combination of static and dynamic features. Evaluations demonstrate that sentence compression is a valuable component of a larger multi-document summarization framework.

Type

a

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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.

Type

a

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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

Type

a

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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.

Type

a