acl acl2010 acl2010-8 acl2010-8-reference knowledge-graph by maker-knowledge-mining
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Author: Asli Celikyilmaz ; Dilek Hakkani-Tur
Abstract: Scoring sentences in documents given abstract summaries created by humans is important in extractive multi-document summarization. In this paper, we formulate extractive summarization as a two step learning problem building a generative model for pattern discovery and a regression model for inference. We calculate scores for sentences in document clusters based on their latent characteristics using a hierarchical topic model. Then, using these scores, we train a regression model based on the lexical and structural characteristics of the sentences, and use the model to score sentences of new documents to form a summary. Our system advances current state-of-the-art improving ROUGE scores by ∼7%. Generated summaries are less rbeydu ∼n7d%an.t a Gnedn more dc sohuemremnatr bieasse adre upon manual quality evaluations.
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