|標題:||HIERARCHICAL THEME AND TOPIC MODEL FOR SUMMARIZATION|
Undergraduate Honors Program of Electrical Engineering and Computer Science
|關鍵字:||Topic model;structural learning;Bayesian nonparametrics;document summarization|
|摘要:||This paper presents a hierarchical summarization model to extract representative sentences from a set of documents. In this study, we select the thematic sentences and identify the topical words based on a hierarchical theme and topic model (H2TM). The latent themes and topics are inferred from document collection. A tree stick-breaking process is proposed to draw the theme proportions for representation of sentences. The structural learning is performed without fixing the number of themes and topics. This H2TM is delicate and flexible to represent words and sentences from heterogeneous documents. Thematic sentences are effectively extracted for document summarization. In the experiments, the proposed H2TM outperforms the other methods in terms of precision, recall and F-measure.|
|期刊:||2013 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)|
|Appears in Collections:||Conferences Paper|
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