A Bag of Tricks for Dialogue Summarization

Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines.

[1]  Dilek Z. Hakkani-Tür,et al.  A keyphrase based approach to interactive meeting summarization , 2008, 2008 IEEE Spoken Language Technology Workshop.

[2]  Gerald Penn,et al.  Summarization of spontaneous conversations , 2006, INTERSPEECH.

[3]  Yun-Nung Chen,et al.  Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts , 2018, 2018 IEEE Spoken Language Technology Workshop (SLT).

[4]  Diyi Yang,et al.  Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization , 2020, EMNLP.

[5]  Alexander H. Waibel,et al.  DIASUMM: Flexible Summarization of Spontaneous Dialogues in Unrestricted Domains , 2000, COLING.

[6]  Hugo Liu,et al.  ConceptNet — A Practical Commonsense Reasoning Tool-Kit , 2004 .

[7]  Myle Ott,et al.  fairseq: A Fast, Extensible Toolkit for Sequence Modeling , 2019, NAACL.

[8]  Jean Carletta,et al.  The AMI meeting corpus , 2005 .

[9]  Nathanael Chambers,et al.  A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories , 2016, NAACL.

[10]  Yejin Choi,et al.  CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning , 2020, EMNLP.

[11]  Weiran Xu,et al.  Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words , 2020, COLING.

[12]  Andreas Stolcke,et al.  The ICSI Meeting Corpus , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[13]  Omer Levy,et al.  BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension , 2019, ACL.

[14]  Aleksander Wawer,et al.  SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization , 2019, EMNLP.

[15]  Osmar R. Zaïane,et al.  ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION) , 2020, SemEval@COLING.

[16]  Jean-Pierre Lorré,et al.  Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization , 2018, ACL.

[17]  Yao Zhao,et al.  PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization , 2020, ICML.

[18]  Ming-Wei Chang,et al.  REALM: Retrieval-Augmented Language Model Pre-Training , 2020, ICML.

[19]  Klaus Zechner,et al.  Automatic Summarization of Open-Domain Multiparty Dialogues in Diverse Genres , 2002, CL.

[20]  Jason Weston,et al.  Personalizing Dialogue Agents: I have a dog, do you have pets too? , 2018, ACL.

[21]  Heng Ji,et al.  Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization , 2019, ACL.

[22]  Roser Morante,et al.  *SEM 2012 Shared Task: Resolving the Scope and Focus of Negation , 2012, *SEMEVAL.

[23]  Yejin Choi,et al.  COMET: Commonsense Transformers for Automatic Knowledge Graph Construction , 2019, ACL.

[24]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[25]  Steve Renals,et al.  Term-Weighting for Summarization of Multi-party Spoken Dialogues , 2007, MLMI.

[26]  Jean Carletta,et al.  Extractive summarization of meeting recordings , 2005, INTERSPEECH.