首页|Aligning Gaussian-Topic with Embedding Network for Summarization Ranking
Aligning Gaussian-Topic with Embedding Network for Summarization Ranking
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NETL
Springer Nature
Query-oriented summarization addresses the problem of information overload and help people get the main ideas within a short time。 Summaries are composed by sentences。 So， the basic idea of composing a salient summary is to construct quality sentences both for user specific queries and multiple documents。 Sentence embedding has been shown effective in summarization tasks。 However， these methods lack of the latent topic structure of contents。 Hence， the summary lies only on vector space can hardly capture multi-topical content。 In this paper， our proposed model incorporates the topical aspects and continuous vector representations， which jointly learns semantic rich representations encoded by vectors。 Then， leveraged by topic filtering and embedding ranking model， the summarization can select desirable salient sentences。 Experiments demonstrate outstanding performance of our proposed model from the perspectives of prominent topics and semantic coherence。
Beijing Institute of Technology, Beijing, China,Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, People's Republic of China
Beijing Institute of Technology, Beijing, China,Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing Institute of Technology, Beijing, China
Aisa-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data
NETL
Springer Nature
