Multi-document Online News Summarization by Learnable Submodular Func

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Ghadimi Alireza; Beigy hamid

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2020 | Issue:6

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Title

Multi-document Online News Summarization by Learnable Submodular Func

Author(s)

Ghadimi Alireza | Beigy hamid

Keywords

Multi-Document Summarization

Submodularity

Neural Network

Weighted Graph

Abstract

With the increasing rate of online information generation and the growth of Internet users, the need for automatic text summarization has become more urgent. In this paper, a Multi-Document Summarization method based on learnable submodular functions is provided. Using Submodularity, it is possible to guarantee the quality of solution. Input text documents are modeled using Weighted Graphs, where nodes and edges represent sentences and similarities between them, respectively. Using this graph, the features that indicate the significance and impact of each sentence are extracted. Significance features consider the value of any sentence, independent of other ones. Modular functions are used to model these features. On the other hand, impact features consider the relationships between sentences. For modeling these features, submodular functions are used. These features are the building blocks of the target function which is equivalent to an ordinary Neural Network. Therefore, using a training set, network learns how to summarize text. After learning phase, the obtained function is used to summarize input texts. The summarizer has been examined using Pasokh and DUC 2004 datasets, and its results are presented.

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APA: Copy

Ghadimi, Alireza, & Beigy, hamid. (2020). Multi-document Online News Summarization by Learnable Submodular Func. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949263/en

Vancouver: Copy

Ghadimi Alireza, Beigy hamid. Multi-document Online News Summarization by Learnable Submodular Func. 2020. Available from: https://sid.ir/paper/949263/en

IEEE: Copy

Alireza Ghadimi, and hamid Beigy, “Multi-document Online News Summarization by Learnable Submodular Func,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2020, [Online]. Available: https://sid.ir/paper/949263/en

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