Summarization of Dialogues and Conversations At Scale (2023)

Abstract: Conversations are the natural communication format for people. This fact has motivated the large body of question answering and chatbot research as a seamless way for people to interact with machines. The conversations between people however, captured as video, audio or private or public written conversations, largely remain untapped as a source of compelling starting point for developing language technology. Summarizing such conversations can be enormously beneficial: automatic minutes for meetings or meeting highlights sent to relevant people can optimize communication in various groups while minimizing demands on people’s time; similarly analysis of conversations in online support groups can provide valuable information to doctors about the patient concerns. Summarizing written and spoken conversation poses unique research challenges—text reformulation, discourse and meaning analysis beyond the sentence, collecting data, and proper evaluation metrics. All these have been revisited by researchers since the emergence of neural approaches as the dominant approach for solving language processing problems. In this tutorial, we will survey the cutting-edge methods for summarization of conversations, covering key sub-areas whose combination is needed for a successful solution.

## introduction conversations are the natural communication format for people. this fact has motivated the large body of question answering and chatbot research as a seamless way for people to interact with machines. the conversations between people however, captured as video, audio or private or public written conversations, largely remain untapped as a source of compelling starting point for developing language technology. summarizing such conversations can be enormously beneficial: automatic minutes for meetings or meeting highlights sent to relevant people can optimize communication in various groups while minimizing demands on people's time; similarly analysis of conversations in online support groups can provide valuable information to doctors about the patient concerns. summarizing written and spoken conversation poses unique research challenges-text reformulation, discourse and meaning analysis beyond the sentence, collecting data, and proper evaluation metrics. all these have been revisited by researchers since the emergence of neural approaches as the dominant approach for solving language processing problems. in this tutorial, we will survey the cutting-edge methods for summarization of conversations, covering key sub-areas whose combination is needed for a successful solution. ## tutorial outline this will be a three-hour tutorial devoted to the cutting-edge topic of conversation summarization. our tutorial will include three sessions. each session will be 40 minutes, followed by 10 minutes for q&a and 10 minutes for a break. each part includes an overview of the corresponding topic and widely used methods and a deep dive into representative research. the detailed tutorial schedule can be found in ## diversity considerations the conversation summarization techniques we introduce is language agnostic. thus, they can be applied to data in various languages and localities with some extent of adaption. code-switch and multilingual models for conversation summarization can scale this work beyond english. our presenter team will share our tutorial with a worldwide audience by promoting it on social media. our presenters span over junior (diyi yang) and senior researchers (chenguang zhu) with a female. diyi is from academia and chenguang is from industry. thus, we have diversified instructors which will also help encourage diverse audience. diyi has experience co-organizing widening nlp workshops at both naacl and acl, and actively works on inviting undergraduate students to research and promoting diversity such as by speaking at ai4all and local high-schools at atlanta. we will work with acl/naacl d&i teams, and consult resources such as the big directory to diversify our audience participation. ## ethics statement certain conversation data might come from private dialogues between people. thus, privacy considerations must be take to ensure all data that is released conforms to regulations and are under consent. as conversations and large-pretrained language models may have bias in various forms, summarization models may contain the same form of bias and should be reviewed and modified if necessary.