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.

Multi-Document Summarization with Iterative Graph-based Algorithms

Abstract: In this paper, we show how a meta-summarizer relying on a layered application of graph-based techniques for single-document summarization can be turned into an effective method for multi-document summarization. Through evaluations performed on standard data sets, we show that this method compares favorably with state-of-the-art techniques for multi-document summarization.

Automatic Multi-document Summarization Based on Clustering and Nonnegative Matrix Factorization

Abstract: In this paper, a novel summarization method that uses nonnegative matrix factorization (NMF) and the clustering method is introduced to extract meaningful sentences relevant to a given query. The proposed method decomposes a sentence into the linear combination of sparse nonnegative semantic features so that it can represent a sentence as the sum of a few semantic features that are comprehensible intuitively. It can improve the quality of document summaries because it can avoid extracting those sentences whose similarities with the query are high but that are meaningless by using the similarity between the query and the semantic features. In addition, the proposed approach uses the clustering method to remove noise and avoid the biased inherent semantics of the documents being reflected in summaries. The method can ensure the coherence of summaries by using the rank score of sentences with respect to semantic features. The experimental results demonstrate that the proposed method has better perfor...

Experimenting Text Summarization Techniques for Contextual Advertising.

Abstract: Contextual advertising systems suggest suitable advertisings to users while surfing the Web. Focusing on text summarization, we propose novel techniques for contextual advertising. Comparative experiments between these techniques and existing ones have been performed.

A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization

Abstract: We consider the problem of using sentence compression techniques to facilitate query-focused multi-document summarization. We present a sentence-compression-based framework for the task, and design a series of learning-based compression models built on parse trees. An innovative beam search decoder is proposed to efficiently find highly probable compressions. Under this framework, we show how to integrate various indicative metrics such as linguistic motivation and query relevance into the compression process by deriving a novel formulation of a compression scoring function. Our best model achieves statistically significant improvement over the state-of-the-art systems on several metrics (e.g. 8.0% and 5.4% improvements in ROUGE-2 respectively) for the DUC 2006 and 2007 summarization task.

Affinity-Preserving Random Walk for Multi-Document Summarization.

Abstract: Multi-document summarization provides users with a short text that summarizes the information in a set of related documents This paper introduces affinity-preserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint of summarization in the random walk process The ROUGE evaluations on DUC 2003 topic-focused summarization task and DUC 2004 generic summarization task show the good performance of our method, which has the best ROUGE-2 recall among the graph-based ranking methods