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.
Papers published on a yearly basis
Papers
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TL;DR: Experimental result means that you can counterpoise the output of TDT by adjusting α value and generate better quality dynamic multi-document summarization.
Abstract: In this paper time characteristics of dynamic multi-documents summarization has been analyzed by the clustering idea in topic detection technologyFrom the change of time information threshold value,differ-ent multi-document clustering and multi-documents summarization for the dynamic Web information data stream can be generatedBy compared with different threshold values,the importance of time information in a dynamic multi-document summarization is understandedExperimental result means that you can counterpoise the output of TDT by adjusting α value and generate better quality dynamic multi-document summarization
1 citations
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TL;DR: Subject-object-predicate triples are firstly extracted from document set, then the edit distance-based clustering and PageRank algorithm are applied to optimize the graph structure and to assign weights to the vertices and links,respectively.
Abstract: Proper processing of the document set based on its semantic structure helps bring about better multi-document summaries.In this paper,subject-object-predicate triples are firstly extracted from document set to construct document semantic graph.Then the edit distance-based clustering and PageRank algorithm are applied to optimize the graph structure and to assign weights to the vertices and links,respectively.Finally,triples with more weighted vertices and links are collected as the summary.Evaluated against the extraction-based summarization in terms of the ROUGE score on a set of manual generated summaries,it shows that the semantic graph-based summarization gained more overlaps with manually created summaries,and the edit distance-based graph structure optimization is positive to the the summarization quality.
1 citations
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01 Jun 2015
TL;DR: This thesis presents a work in progress to define an algorithm to extract truly significant keywords which might have lost its significance if subjected to the current keyword extraction algorithms.
Abstract: Summarization is the process of reducing a text document in order to create a summary that retains the most important points of the original document. As the problem of information overload has grown, and as the quantity of data has increased, so has interest in automatic summarization. Extractive summary works on the given text to extract sentences that best convey the message hidden in the text. Most extractive summarization techniques revolve around the concept of indexing keywords and extracting sentences that have more keywords than the rest. Keyword extraction usually is done by extracting important words having a higher frequency than others, with stress on important. However the current techniques to handle this importance include a stop list which might include words that are critically important to the text. In this thesis, I present a work in progress to define an algorithm to extract truly significant keywords which might have lost its significance if subjected to the current keyword extraction algorithms.
1 citations
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11 Aug 2002TL;DR: An integration of supervised learning with unsupervised learning to deal with human biases in summarization is proposed and empirically motivates an use of probabilistic decision tree within the clustering framework to account for the variation as well as regularity in human created summaries.
Abstract: The paper proposes and empirically motivates an integration of supervised learning with unsupervised learning to deal with human biases in summarization. In particular, we explore the use of probabilistic decision tree within the clustering framework to account for the variation as well as regularity in human created summaries.
1 citations
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TL;DR: A new method that used Grid Model and dynamic programming to calculate n-grams for generating the best sentence compression and the experimental results showed that the method really effective and the text is grammatically, coherence and concise.
Abstract: Sentence compression is a valuable task in the framework of text summarization. In previous works, the sentence is reduced by removing redundant words or phrases from original sentence and tries to remain information. In this paper, we propose a new method that used Grid Model and dynamic programming to calculate n-grams for generating the best sentence compression. These reduced sentences are combined to text summarization. The experimental results showed that our method really effective and the text is grammatically, coherence and concise.
1 citations