Abstract
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.
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Notes
- 1.
In DUC, the word-based query is also called “title”, such as “New hydroelectric projects”.
- 2.
In DUC, the sentence-based query is also called “narrative”, such as “What hydroelectric projects are planned or in progress and what problems are associated with them?”.
- 3.
- 4.
In DUC, the query is also called “narrative” or “topic”.
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Acknowledgments
The work was supported by National Nature Science Foundation of China (Grant No. 61602036), National Basic Research Program of China (973 Program, Grant No. 2013CB329303), Beijing Advanced Innovation Center for Imaging Technology (BAICIT-2016007).
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Wei, L., Huang, H., Gao, Y., Wei, X., Feng, C. (2017). Aligning Gaussian-Topic with Embedding Network for Summarization Ranking. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_46
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