Proceedings ArticleDOI
Subtopic-Focused Sentence Scoring in Multi-document Summarization
Li Sujian,Qu Weiguang +1 more
- pp 98-104
TLDR
This paper proposes a subtopic- focused model to score sentences in the extractive summarization task via a hierarchical Bayesian model, through which sentences are scored and extracted as summary.Abstract:
In previous works, subtopics are seldom mentioned in multi-document summarization while only one topic is focused to extract summary. In this paper, we propose a subtopic- focused model to score sentences in the extractive summarization task. Different with supervised methods, it does not require costly manual work to form the training set. Multiple documents are represented as mixture over subtopics, denoted by term distributions through unsupervised learning. Our method learns the subtopic distribution over sentences via a hierarchical Bayesian model, through which sentences are scored and extracted as summary. Experiments on DUC 2006 data are performed and the ROUGE evaluation results show that the proposed method can reach the state-of-the-art performance.read more
References
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Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Proceedings ArticleDOI
Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies
TL;DR: A multi-document summarizer, called MEAD, is presented, which generates summaries using cluster centroids produced by a topic detection and tracking system and two new techniques, based on sentence utility and subsumption, are described.
Posted Content
Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies
TL;DR: This article presented a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system, and also described two new techniques, based on sentence utility and subsumption, which have applied to the evaluation of both single and multiple document summaries.
Proceedings Article
Manifold-ranking based topic-focused multi-document summarization
TL;DR: A novel extractive approach based on manifold-ranking of sentences to this summarization task can significantly outperform existing approaches of the top performing systems in DUC tasks and baseline approaches.
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