A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization (2021)

Abstract: Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding. To address this question, we propose a novel end-to-end Transformer-based model FinDS for abstractive dialogue summarization that leverages Finer-grain universal Dialogue semantic Structures to model dialogue and generates better summaries. Experiments on the SAMsum dataset show that FinDS outperforms various dialogue summarization approaches and achieves new state-of-the-art (SOTA) ROUGE results. Finally, we apply FinDS to a more complex scenario, showing the robustness of our model. We also release our source code.

## introduction the field of abstractive summarization has recently seen impressive progress in document scenarios, while less attention has been paid to dialogue summarization. previous research on dialogue summarization is based on successful document summarization models @xcite @xcite which model the dialogue in a crude and straight manner. taking the example of such factual errors indicate that it is not suitable to transfer the document summarization model to the dialogues summarization model. this is mainly based on the understanding of these potential risks of dialogue scenario, recent work focuses on developing methods suitable for this dialogue summarization: @xcite developes an unsupervised multi-sentence compression algorithm, while @xcite proposes a self-adaptive learning model to learn the segmentation strategy of utterances and topics. these methods are modified based on document summarization methods. others also introduces some models designing specially for dialogue summarization: @xcite al. (2019b); li et al. (2019); zou et al. (2020) leverages the topic information that flows in the dialogue to help generate topic-aware summaries, goo and chen (2018); @xcite manually annotates the dialogue to construct some prior structural knowledge which helps the model obtain a more informative and accurate context. chen and yang (2020) introduces two model-annotated dialogue structural views to help encode the utterances. unfortunately, these jobs remain at a coarse level that can not correctly capture the relationships between speakers and utterances and topics. some of them are time and labor-consuming or contain error superposition because of some handcrafted or modelbased label. accordingly, we propose a novel end-to-end transformer-based @xcite the itss interacts with the information from one speaker to other speakers except for himself, as figure 1(d) shows. all these structures are constructed based on the universal characteristic of dialogue previously in an automatic method. with the help of these finer-grain universal dialogue semantic structures, our finds model performs effectively and robustly for dialogue summarization. our contributions are three-fold: ## method to capture the core topics and build up a correct dependency between speakers and utterances at a finer-grain level, we propose to model the complex dependencies in the dialogue with the following procedures: (1) constructing ius, gts, inss, and itss in an automatic way (section 3.2). (2) encoding the dialogue by modifying self-attention processing with four dialogue semantic structures (section 3.3). ## conclusion in this paper, we develop a novel end-to-end transformer-based model finds for abstractive dialogue summarization that leverages finer-grain universal dialogue semantic structures to model dialogue and generates better summaries. experiments have shown finds achieves new sota results on the rouge metrics. more importantly, finds proves its robustness and effectiveness for every structure in the complex dialogue scenario.