A Combined Extractive With Abstractive Model for Summarization

• Published in: IEEE access Vol. 9; pp. 43970 - 43980
• Main Authors: Liu, Wenfeng, Gao, Yaling, Li, Jinming, Yang, Yuzhen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: abstractive summarization; beam search; Deep learning; Extractive summarization; Feature extraction; Neural networks; Reinforcement learning; Search algorithms; Semantics; Syntactics; word embeddings
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3066484

Aiming at the difficulties in document-level summarization, this paper presents a two-stage, extractive and then abstractive summarization model. In the first stage, we extract the important sentences by combining sentences similarity matrix (only used for the first time) or pseudo-title, which takes full account of the features (such as sentence position, paragraph position, and more.). To extract coarse-grained sentences from a document, and considers the sentence differentiation for the most important sentences in the document. The second stage is abstractive, and we use beam search algorithm to restructure and rewrite these syntactic blocks of these extracted sentences. Newly generated summary sentence serves as the pseudo-summary of the next round. Globally optimal pseudo-title acts as the final summarization. Extensive experiments have been performed on the corresponding data set, and the results show our model can obtain better results.