DeepSumm: Exploiting topic models and sequence to sequence networks for extractive text summarization

作者：

Highlights：

• DeepSumm for extractive summarization based on seq2seq networks.

• Sentence encoded with probabilistic topic distributions and word embeddings RNNs.

• Proposed Sentence Novelty Score, based on sentence content and topic embeddings.

• Seq2seq attention networks extract content and topic Scores.

• Summary generated using four sentence scores: Content, Topic, Novelty and Position.

摘要

•DeepSumm for extractive summarization based on seq2seq networks.•Sentence encoded with probabilistic topic distributions and word embeddings RNNs.•Proposed Sentence Novelty Score, based on sentence content and topic embeddings.•Seq2seq attention networks extract content and topic Scores.•Summary generated using four sentence scores: Content, Topic, Novelty and Position.

论文关键词：Text summarization,Extractive,Seq2seq,Attention networks,Topic models

论文评审过程：Received 5 August 2020, Revised 27 July 2022, Accepted 4 August 2022, Available online 13 August 2022, Version of Record 30 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118442