Research Area:  Machine Learning

Abstract:

Abstractive Text Summarization (ATS), which is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. It is very difficult and time consuming for human beings to manually summarize large documents of text. In this paper, we propose an LSTM-CNN based ATS framework (ATSDL) that can construct new sentences by exploring more fine-grained fragments than sentences, namely, semantic phrases. Different from existing abstraction based approaches, ATSDL is composed of two main stages, the first of which extracts phrases from source sentences and the second generates text summaries using deep learning. Experimental results on the datasets CNN and DailyMail show that our ATSDL framework outperforms the state-of-the-art models in terms of both semantics and syntactic structure, and achieves competitive results on manual linguistic quality evaluation.

Keywords:  

Author(s) Name:  Shengli Song, Haitao Huang & Tongxiao Ruan

Publisher name:  Springer

DOI:  10.1007/s11042-018-5749-3

Volume Information:  volume 78, pages857–875 (2019)

Paper Link:   https://link.springer.com/article/10.1007/s11042-018-5749-3