Topic
Multi-document summarization
About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.
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Papers
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21 Mar 2005
TL;DR: An information filtering method that selects from a set of documents their most significant excerpts in relation to a user profile based on both structured profiles and a topical analysis of documents is presented.
Abstract: In this article, we present an information filtering method that selects from a set of documents their most significant excerpts in relation to a user profile. This method relies on both structured profiles and a topical analysis of documents. The topical analysis is also used for expanding a profile in relation to a particular document by selecting the terms of the document that are closely linked to those of the profile. This expansion is a way for selecting in a more reliable way excerpts that are linked to profiles but also for selecting excerpts that may bring new and interesting information about their topics. This method was implemented by the REDUIT system, which was successfully evaluated for document filtering and passage extraction.
2 citations
Journal Article•
TL;DR: This paper describes a multi-document summarization method based on localtopics identification and extraction, where the size of summarization is determined according to content of multiple documents, which becomes general and concise.
2 citations
Book•
31 Jan 2014
TL;DR: Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding evaluates some of the existing approaches to information retrieval and summarization of digital documents, as well as current research and future developments.
Abstract: The prevalence of digital documentation presents some pressing concerns for efficient information retrieval in the modern age. Readers want to be able to access the information they desire without having to search through a mountain of unrelated data, so algorithms and methods for effectively seeking out pertinent information are of critical importance.Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding evaluates some of the existing approaches to information retrieval and summarization of digital documents, as well as current research and future developments. This book serves as a sounding board for students, educators, researchers, and practitioners of information technology, advancing the ongoing discussion of communication in the digital age.
2 citations
TL;DR: This paper proposes a novel biased diversity ranking model, named ManifoldDivRank, for query-sensitive summarization tasks, and finds that the top-ranked sentences discovered not only enjoy query-oriented high prestige, more importantly, they are dissimilar with each other.
Abstract: Query-oriented multi-document summarization (QMDS) attempts to generate a concise piece of text byextracting sentences from a target document collection, with the aim of not only conveying the key content of that corpus, also, satisfying the information needs expressed by that query. Due to its great applicable value, QMDS has been intensively studied in recent decades. Three properties are supposed crucial for a good summary, i.e., relevance, prestige and low redundancy (orso-called diversity). Unfortunately, most existing work either disregarded the concern of diversity, or handled it with non-optimized heuristics, usually based on greedy sentences election. Inspired by the manifold-ranking process, which deals with query-biased prestige, and DivRank algorithm which captures query-independent diversity ranking, in this paper, we propose a novel biased diversity ranking model, named ManifoldDivRank, for query-sensitive summarization tasks. The top-ranked sentences discovered by our algorithm not only enjoy query-oriented high prestige, more importantly, they are dissimilar with each other. Experimental results on DUC2005and DUC2006 benchmark data sets demonstrate the effectiveness of our proposal.
2 citations
TL;DR: The improved TextRank method, integrating user demands and sentence features into the model, makes the results of text summarization closer to the theme of the article and more able to meet the user demand.
Abstract: With the rapid development of we-media information dissemination, WeChat official accounts platform has become an important way for people to obtain health related knowledge. However, the platform information is redundant, miscellaneous, and overloaded. In order to meet the increasingly accurate and efficient knowledge service needs of users, reorganizing and aggregating document knowledge resources is effective. If we use the way of artificial recognition to filter information, it will inevitably cause huge labor and time cost, and the effect is very little in front of massive articles. This paper proposes a text summarization method for the WeChat platform based on improved TextRank that takes into account both user demands and sentence features during the summarization process. The data source crawled from the Sogou WeChat platform. The results show that the TextRank algorithm has obvious hints on the accuracy of text summarization extraction after fusing the Word2vec model. The improved TextRank method, integrating user demands and sentence features into the model, makes the results of text summarization closer to the theme of the article and more able to meet the user demand. According to the complexity of the algorithm, this method is not suitable for the automatic summarization of long or multiple documents.
2 citations