Discourse-Aware Neural Extractive Text Summarization
Jiacheng Xu, Zhe Gan, Yu Cheng, Jun Liu · 2020
Recently BERT has been adopted for document encoding in state-of-the-art text summarization models.However, sentence-based extractive models often result in redundant or uninformative phrases in the extracted summaries.Also, long-range dependencies throughout a document are not well captured by BERT, which is pre-trained on sentence pairs instead of documents.To address these issues, we present a discourse-aware neural summarization model -DISCOBERT 1 .DISCOBERT extracts sub-sentential discourse units (instead of sentences) as candidates for extractive selection on a finer granularity.To capture the long-range dependencies among discourse units, structural discourse graphs are constructed based on RST trees and coreference mentions, encoded with Graph Convolutional Networks.Experiments show that the proposed model outperforms state-of-the-art methods by a significant margin on popular summarization benchmarks compared to other BERT-base models.