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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TL;DR: Four modern systems of automatic text summarization are tested on the basis of a model vocabulary composed by subjects and principles for evaluation of the efficiency of the current systems are described.
Abstract: Four modern systems of automatic text summarization are tested on the basis of a model vocabulary composed by subjects. Distribution of terms of the vocabulary in the source text is compared with their distribution in summaries of different length generated by the systems. Principles for evaluation of the efficiency of the current systems of automatic text summarization are described.
30 citations
TL;DR: A novel vision and a novel system for movies summarization by means of IM(S)^2, people generate on the fly customized video summaries responding to their preferences responding to the user's preferences.
30 citations
25 Jun 2007
TL;DR: This work focuses on semantic relations among event terms and builds up event term graph, upon which relevant terms are grouped into clusters, which shows encouraging improvement over the well-known PageRank-based summarization.
Abstract: Event-based summarization extracts and organizes summary sentences in terms of the events that the sentences describe. In this work, we focus on semantic relations among event terms. By connecting terms with relations, we build up event term graph, upon which relevant terms are grouped into clusters. We assume that each cluster represents a topic of documents. Then two summarization strategies are investigated, i.e. selecting one term as the representative of each topic so as to cover all the topics, or selecting all terms in one most significant topic so as to highlight the relevant information related to this topic. The selected terms are then responsible to pick out the most appropriate sentences describing them. The evaluation of clustering-based summarization on DUC 2001 document sets shows encouraging improvement over the well-known PageRank-based summarization.
30 citations
02 Feb 2015
TL;DR: This paper addresses the problem of summarizing the micro-reviews of an entity, such that the summary is representative, compact, and readable, and forms the summarization problem as that of synthesizing a new ``review'' using snippets of full-text reviews.
Abstract: Micro-reviews is a new type of user-generated content arising from the prevalence of mobile devices and social media in the past few years. Micro-reviews are bite-size reviews (usually under 200 characters), commonly posted on social media or check-in services, using a mobile device. They capture the immediate reaction of users, and they are rich in information, concise, and to the point. However, the abundance of micro-reviews, and their telegraphic nature make it increasingly difficult to go through them and extract the useful information, especially on a mobile device. In this paper, we address the problem of summarizing the micro-reviews of an entity, such that the summary is representative, compact, and readable. We formulate the summarization problem as that of synthesizing a new ``review'' using snippets of full-text reviews. To produce a summary that naturally balances compactness and representativeness, we work within the Minimum Description Length framework. We show that finding the optimal summary is NP-hard, and we consider approximation and heuristic algorithms. We perform a thorough evaluation of our methodology on real-life data collected from Foursquare and Yelp. We demonstrate that our summaries outperform individual reviews, as well as existing summarization approaches.
30 citations
01 Feb 2014
TL;DR: The feasibility of using the ontology in solving multi- document summarization problems in the domain of disaster management is explored and it is demonstrated that ontology-based multi-document summarization methods outperform other baselines in terms of the summary quality.
Abstract: Domain ontology, as a conceptual model, provides a meaningful framework for semantic representation of textual information. In this paper, we explore the feasibility of using the ontology in solving multi-document summarization problems in the domain of disaster management. We provide an empirical study of different approaches in which the ontology has been used for summarization tasks. Extensive experiments on a collection of press releases relevant to Hurricane Wilma in 2005 demonstrate that ontology-based multi-document summarization methods outperform other baselines in terms of the summary quality.
30 citations