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View all- Al-Sabahi KZuping ZNadher M(2018)A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS)IEEE Access10.1109/ACCESS.2018.28291996(24205-24212)Online publication date: 2018
More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as reflected in reader comments. In this ...
Compared with VSM (Vector Space Model) and graph-ranking models, LDA (Latent Dirichlet Allocation) Model can discover latent topics in the corpus and latent topics are beneficial to use sentence-ranking mechanisms to form a good summary. In the paper, ...
In recent years graph-ranking based algorithms have been proposed for single document summarization and generic multi-document summarization. The algorithms make use of the “votings” or “recommendations” between sentences to evaluate the ...
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