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: This paper proposes a new summarization task, namely sequential summarization, which aims to provide a serial of chronologically ordered short sub-summaries for a trending topic in order to provided a complete story about the development of the topic while retaining the order of information presentation.
Abstract: As an information delivering platform, Twitter collects millions of tweets every day. However, some users, especially new users, often find it difficult to understand trending topics in Twitter when confronting the overwhelming and unorganized tweets. Existing work has attempted to provide a short snippet to explain a topic, but this only provides limited benefits and cannot satisfy the users' expectations. In this paper, we propose a new summarization task, namely sequential summarization, which aims to provide a serial of chronologically ordered short sub-summaries for a trending topic in order to provide a complete story about the development of the topic while retaining the order of information presentation. Different from the traditional summarization task, the numbers of sub-summaries for different topics are not fixed. Two approaches, i.e., stream-based and semantic-based approaches, are developed to detect the important subtopics within a trending topic. Then a short sub-summary is generated for each subtopic. In addition, we propose three new measures to evaluate the position-aware coverage, sequential novelty and sequence correlation of the system-generated summaries. The experimental results based on the proposed evaluation criteria have demonstrated the effectiveness of the proposed approaches.
34 citations
20 Jan 2007
TL;DR: A new summarization method, which uses non-negative matrix factorization (NMF) and K-means clustering, is introduced to extract meaningful sentences from multi-documents and has better performance than other methods using the LSA, the Kmeans, and the NMF.
Abstract: In this paper, a new summarization method, which uses non-negative matrix factorization (NMF) and K-means clustering, is introduced to extract meaningful sentences from multi-documents. The proposed method can improve the quality of document summaries because the inherent semantics of the documents are well reflected by using the semantic features calculated by NMF and the sentences most relevant to the given topic are extracted efficiently by using the semantic variables derived by NMF. Besides, it uses K-means clustering to remove noises so that it can avoid the biased inherent semantics of the documents to be reflected in summaries. We perform detail experiments with the well-known DUC test dataset. The experimental results demonstrate that the proposed method has better performance than other methods using the LSA, the Kmeans, and the NMF.
33 citations
TL;DR: This paper proposes a population-based multicriteria optimization method with multiple objective functions which generates an extractive generic summary with maximum relevance and minimum redundancy by representing each sentence of the input document as a vector of words in Proper Noun,Noun, Verb and Adjective set.
Abstract: Multi-document summarization is the process of extracting salient information from a set of source texts and present that information to the user in a condensed form. In this paper, we propose a multi-document summarization system which generates an extractive generic summary with maximum relevance and minimum redundancy by representing each sentence of the input document as a vector of words in Proper Noun, Noun, Verb and Adjective set. Five features, such as TF_ISF, Aggregate Cross Sentence Similarity, Title Similarity, Proper Noun and Sentence Length associated with the sentences, are extracted, and scores are assigned to sentences based on these features. Weights that can be assigned to different features may vary depending upon the nature of the document, and it is hard to discover the most appropriate weight for each feature, and this makes generation of a good summary a very tough task without human intelligence. Multi-document summarization problem is having large number of decision parameters and number of possible solutions from which most optimal summary is to be generated. Summary generated may not guarantee the essential quality and may be far from the ideal human generated summary. To address this issue, we propose a population-based multicriteria optimization method with multiple objective functions. Three objective functions are selected to determine an optimal summary, with maximum relevance, diversity, and novelty, from a global population of summaries by considering both the statistical and semantic aspects of the documents. Semantic aspects are considered by Latent Semantic Analysis (LSA) and Non Negative Matrix Factorization (NMF) techniques. Experiments have been performed on DUC 2002, DUC 2004 and DUC 2006 datasets using ROUGE tool kit. Experimental results show that our system outperforms the state of the art works in terms of Recall and Precision.
33 citations
24 Oct 2016
TL;DR: This study simulates human like methods by integrating fuzzy logic with traditional statistical approaches and indicates that the approach can deal with uncertainty and achieve better results when compared with existing methods.
Abstract: Due to the high volume of information and electronic documents on the Web, it is almost impossible for a human to study, research and analyze this volume of text. Summarizing the main idea and the major concept of the context enables the humans to read the summary of a large volume of text quickly and decide whether to further dig into details. Most of the existing summarization approaches have applied probability and statistics based techniques. But these approaches cannot achieve high accuracy. We observe that attention to the concept and the meaning of the context could greatly improve summarization accuracy, and due to the uncertainty that exists in the summarization methods, we simulate human like methods by integrating fuzzy logic with traditional statistical approaches in this study. The results of this study indicate that our approach can deal with uncertainty and achieve better results when compared with existing methods.
33 citations
TL;DR: A novel multi-document summarization system called FoDoSu, or Folksonomy-based Multi-Document Summarization, that employs the tag clusters used by Flickr, a Folksonomic system, for detecting key sentences from multiple documents is proposed.
Abstract: Multi-document summarization techniques aim to reduce documents into a small set of words or paragraphs that convey the main meaning of the original document. Many approaches to multi-document summarization have used probability-based methods and machine learning techniques to simultaneously summarize multiple documents sharing a common topic. However, these techniques fail to semantically analyze proper nouns and newly-coined words because most depend on an out-of-date dictionary or thesaurus. To overcome these drawbacks, we propose a novel multi-document summarization system called FoDoSu, or Folksonomy-based Multi-Document Summarization, that employs the tag clusters used by Flickr, a Folksonomy system, for detecting key sentences from multiple documents. We first create a word frequency table for analyzing the semantics and contributions of words using the HITS algorithm. Then, by exploiting tag clusters, we analyze the semantic relationships between words in the word frequency table. Finally, we create a summary of multiple documents by analyzing the importance of each word and its semantic relatedness to others. Experimental results from the TAC 2008 and 2009 data sets demonstrate the improvement of our proposed framework over existing summarization systems.
33 citations