STRUDEL : Structured Dialogue Summarization for Dialogue Comprehension (2022)

Abstract: Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system’s performance on other important dialogue comprehension tasks. In this paper, we propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization (STRUDEL) - that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. In contrast to the holistic approach taken by the traditional free-form abstractive summarization task for dialogues, STRUDEL aims to decompose and imitate the hierarchical, systematic and structured mental process that we human beings usually go through when understanding and analyzing dialogues, and thus has the advantage of being more focused, specific and instructive for dialogue comprehension models to learn from. We further introduce a new STRUDEL dialogue comprehension modeling framework that integrates STRUDEL into a dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension ability. In our empirical experiments on two important downstream dialogue comprehension tasks - dialogue question answering and dialogue response prediction - we demonstrate that our STRUDEL dialogue comprehension models can significantly improve the dialogue comprehension performance of transformer encoder language models.

## introduction in natural language processing, abstractive dialogue summarization @xcite has long been viewed as an important standalone task, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an nlp system's performance on other important dialogue comprehension tasks. when performing language understanding, a very natural and effective first step that human beings usually take in their mental process is to try to summarize the main content of a piece of text, usually from multiple perspectives each focusing a different aspect of the text. this is especially true when human readers or speakers are trying to understand a dialogue or a conversation, which involve multi-turn exchange of information following a general theme, topic or storyline. therefore, we would like to ask the following question -can the task of abstractive dialogue summarization also help nlp models to learn to perform better dialogue comprehension? in this paper, we propose a novel type of dialogue summarization task -structured dialogue summarization (strudel 1 ) -that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. in contrast to the holistic approach taken by the traditional freeform abstractive summarization task for dialogues, strudel aims to decompose and imitate the hierarchical, systematic and structured mental process that we human beings usually go through when understanding and analyzing dialogues. then we further introduce a new dialogue comprehension model that integrates strudel into a dialogue reasoning module over transformer encoder language models. our empirical experiment results shows that strudel is indeed very effective in providing transformer language models with better support for reasoning and inference over challenging downstream dialogue comprehension tasks such as dialogue question answering and response prediction and improving their performance. ## human annotations of strudel our proposed new task of structured dialogue summarization (strudel) opens up a gateway for language models to observe, imitate and learn from the structured human mental process of sys-tematic dialogue understanding. but in order to actually infuse these valuable human-guided structural priors regarding dialogue understanding into language models through the task of strudel, we first need to collect high-quality supervision information from empirical human demonstration of performing the strudel task. therefore, for this purpose, we collect a set of human annotations of strudel over 400 dialogues sampled from two widely used dialogue comprehension datasetsthe mutual @xcite dataset for dialogue response prediction and the dream @xcite dataset for dialogue question answering. in our collection of strudel human annotations, each sampled dialogue is manually annotated with its complete set of strudel summarization with all 16 strudel entries (can contain 'n/a') by a human annotator following the annotation protocols (see section 4.2). ## modeling approach in this section, we describe our main modeling approach that uses structured dialogue summarization (strudel) to improve pre-trained language model's ability of dialogue comprehension. ## experiments 6.1 transformer encoder models in our experiment, we use two widely-used transformer encoder language models -roberta @xcite and albert @xcite ) -as the backbone transformer encoder in our strudel dialogue comprehension modeling framework. ## conclusion in this paper, we presented strudel (structured dialogue summarization) -a novel type of dialogue summarization task that can help pretrained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. in contrast to the traditional free-form abstractive summarization task for dialogues, strudel provides a more comprehensive digest over multiple important aspects of a dialogue and has the advantage of being more focused, specific and instructive for dialogue comprehension models to learn from. in addition, we also introduced a new strudel dialogue comprehension modeling framework that integrates strudel into a dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension ability. our empirical experiments on the tasks of dialogue question answering and dialogue response prediction confirmed that our strudel dialogue comprehension modeling framework can significantly improve the dialogue comprehension performance of transformer encoder language models.