Proceedings of the 2019 Conference of the North 2019
DOI: 10.18653/v1/n19-1397
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Jointly Extracting and Compressing Documents with Summary State Representations

Abstract: We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The proposed model offers a balance that sidesteps the difficulties in abstractive methods while generating more concise summaries than extractive methods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training, and does not require length constraints typical to extractive summarization. The model achieves st… Show more

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Cited by 46 publications
(55 citation statements)
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References 29 publications
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“…All prior results reported on the arXiv and Pubmed benchmark are obtained from Cohan et al (2018), except for the Bottom-up model 2 (Gehrmann et al, 2018). Similarly, prior results for the BigPatent dataset are obtained from (Sharma et al, 2019) and Newsroom from (Grusky et al, 2018a) and (Mendes et al, 2019). These methods include LexRank (Erkan and Radev, 2004), SumBasic ( Vanderwende et al, 2007), LSA (Steinberger and Jezek, 2004), Attention-Seq2Seq (Nallapati et al, 2016a;Chopra et al, 2016), Pointer-Generator Seq2Seq (See et al, 2017), Discourse-aware, which is a hierarchical extension to the pointer generator model, (Cohan et al, 2018), Sent-rewriting (Chen and Bansal, 2018), RNN-Ext (Chen and Bansal, 2018), Exconsumm (Mendes et al, 2019).…”
Section: Results and Analysismentioning
confidence: 99%
“…All prior results reported on the arXiv and Pubmed benchmark are obtained from Cohan et al (2018), except for the Bottom-up model 2 (Gehrmann et al, 2018). Similarly, prior results for the BigPatent dataset are obtained from (Sharma et al, 2019) and Newsroom from (Grusky et al, 2018a) and (Mendes et al, 2019). These methods include LexRank (Erkan and Radev, 2004), SumBasic ( Vanderwende et al, 2007), LSA (Steinberger and Jezek, 2004), Attention-Seq2Seq (Nallapati et al, 2016a;Chopra et al, 2016), Pointer-Generator Seq2Seq (See et al, 2017), Discourse-aware, which is a hierarchical extension to the pointer generator model, (Cohan et al, 2018), Sent-rewriting (Chen and Bansal, 2018), RNN-Ext (Chen and Bansal, 2018), Exconsumm (Mendes et al, 2019).…”
Section: Results and Analysismentioning
confidence: 99%
“…Ultilizing representations of partially generated summaries is relatively less studied in summarization. Mendes et al (2019) proposed to dynamically model the generated summary using an LSTM to iteratively increment summaries based on previously extracted information. used a feedforward neural network driven by hand-curated features capturing the prevalence of domain subtopics in the source and the summary.…”
Section: Related Workmentioning
confidence: 99%
“…For example, we don't pretrain HiBERT from scratch for document modeling as in . Instead, we initialize our HiBERT models with publicly available RoBERTa checkpoints following the superior performance of (Zhang et al, 2018) 41.05 18.77 37.54 Refresh (Narayan et al, 2018b) 41.00 18.80 37.70 BanditSum (Dong et al, 2018) 41.50 18.70 37.60 NeuSUM (Zhou et al, 2018) 41.59 19.01 37.98 ExConSum (Mendes et al, 2019) 41.70 18.60 37.80 JECS (Xu and Durrett, 2019) 41.70 18.50 37.90 LSTM+PN (Zhong et al, 2019b) 41.85 18.93 38.13 HER (Luo et al, 2019) 42.30 18.90 37.60 HiBERT 42.37 19.95 38.83 PNBERT (Zhong et al, 2019a) 42.69 19.60 38.85 BERTSum (Liu and Lapata, 2019b) 42 RoBERTaSum over BERTSum. We use different number of layers in the document encoder (L doc = 3) and in the sentence encoder (L sent = 9), as opposed to equal number of layers (L = 6) in both encoders of .…”
Section: Stepwise Etcsummentioning
confidence: 99%
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“…Some effort has been made to combine these two branches. Most existing works use the extract-then-abstract framework that first extracts the summary-worthy sentences and then abstracts each of them (Dong et al, 2018;Mendes et al, 2019;Chen and Bansal, 2018). However, they suffer from an information loss in abstract stage, since all the sentence is compressed and pruned without a distinguish.…”
Section: Rewritementioning
confidence: 99%