Guiding Abstractive Dialogue Summarization with Content Planning (2022)

Abstract: Abstractive dialogue summarization has recently been receiving more attention. We propose a coarse-to-fine model for generating abstractive dialogue summaries, and introduce a fact-aware reinforcement learning (RL) objective that improves the fact consistency between the dialogue and the generated summary. Initially, the model generates the predicate-argument spans of the dialogue, and then generates the final summary through a fact-aware RL objective. Extensive experiments and analysis on two benchmark datasets demonstrate that our proposed method effectively improves the quality of the generated summary, especially in coherence and consistency.

## introduction with the prevalence of dialogue texts, new challenges have arisen for abstractive dialogue summarization @xcite . dialogue are often informal and backchanneling, with the salient information and speaker interactions scattered across the whole chat @xcite . however, existing methods (goo and @xcite struggle to maintain factual consistency between dialogue and summary, mainly due to the failure of capturing interactions between plot points. as a result, we propose an abstractive dialogue summarization model that decomposes the problem into a two-step coarse-to-fine generation problem (figure 1 ). we first generate a series of predicateargument spans as content plan. we use semantic role labeling (srl), which focuses on modeling the skeleton of a sentence, to generate predicateargument spans. it provides a weakly supervised signal and is easier for the model to learn dependencies across events. we then feed both the dialogue and content plan to the dual-encoder model, and train it with the fact regularization objective. we evaluate the proposed model on two benchmarks: (i) samsum corpus @xcite , which is a large-scale chat summarization corpus, and (ii) dialogsum corpus @xcite , which is a real-life scenario dialogue summarization dataset. by comparison to previous approaches, our model provides a better generation quality judgment both by humans and by automatic evaluations. furthermore, the results show that the outputs of our model are highly consistent and coherent. in summary, we make the following contributions in this paper: to a comprehensive case study and human evaluation, our model can achieve a more coherent and consistent summary. ## conclusion and future work in this paper, we explore the helpfulness of srl-based content plan composed of predicateargument spans and propose a fact-aware rl training process for the dialogue summarization task. we observe that the use of semantic roles can improve the performance of the bart architecture. in the future, we plan to directly integrate semantic role information into other pretrained large generative models like gpt-3 and t5 to further improve the performance.