2 research outputs found
Discourse-Aware Neural Extractive Text Summarization
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. 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.Comment: To appear at ACL 2020; Code available at
https://github.com/jiacheng-xu/DiscoBER
Heterogeneous Graph Neural Networks for Extractive Document Summarization
As a crucial step in extractive document summarization, learning
cross-sentence relations has been explored by a plethora of approaches. An
intuitive way is to put them in the graph-based neural network, which has a
more complex structure for capturing inter-sentence relationships. In this
paper, we present a heterogeneous graph-based neural network for extractive
summarization (HeterSumGraph), which contains semantic nodes of different
granularity levels apart from sentences. These additional nodes act as the
intermediary between sentences and enrich the cross-sentence relations.
Besides, our graph structure is flexible in natural extension from a
single-document setting to multi-document via introducing document nodes. To
our knowledge, we are the first one to introduce different types of nodes into
graph-based neural networks for extractive document summarization and perform a
comprehensive qualitative analysis to investigate their benefits. The code will
be released on GithubComment: Accepted by ACL202