Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization

• URL: http://arxiv.org/abs/2209.00278v1
• Date: Thu, 1 Sep 2022 07:51:46 GMT
• Title: Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization
• Authors: Hyunjae Lee, Jaewoong Yun, Hyunjin Choi, Seongho Joe, Youngjune L. Gwon
• Abstract summary: We introduce self-supervised methods to compensate shortcomings to train a dialogue summarization model. Our principle is to detect incoherent information flows using pretext dialogue text to enhance BERT's ability to contextualize the dialogue text representations.
• Score: 4.226093500082746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contextualized word embeddings can lead to state-of-the-art performances in natural language understanding. Recently, a pre-trained deep contextualized text encoder such as BERT has shown its potential in improving natural language tasks including abstractive summarization. Existing approaches in dialogue summarization focus on incorporating a large language model into summarization task trained on large-scale corpora consisting of news articles rather than dialogues of multiple speakers. In this paper, we introduce self-supervised methods to compensate shortcomings to train a dialogue summarization model. Our principle is to detect incoherent information flows using pretext dialogue text to enhance BERT's ability to contextualize the dialogue text representations. We build and fine-tune an abstractive dialogue summarization model on a shared encoder-decoder architecture using the enhanced BERT. We empirically evaluate our abstractive dialogue summarizer with the SAMSum corpus, a recently introduced dataset with abstractive dialogue summaries. All of our methods have contributed improvements to abstractive summary measured in ROUGE scores. Through an extensive ablation study, we also present a sensitivity analysis to critical model hyperparameters, probabilities of switching utterances and masking interlocutors.

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