ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining
This addresses the problem of summarizing diverse online conversations for researchers and practitioners, though it is incremental as it builds on existing summarization methods.
The authors tackled the lack of standardized datasets for summarizing online conversations by creating four new datasets using an issues-viewpoints-assertions framework and benchmarked state-of-the-art models, achieving comparable or improved results with argument mining.
While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles. This research gap is due, in part, to the lack of standardized datasets for summarizing online discussions. To address this gap, we design annotation protocols motivated by an issues--viewpoints--assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data. To create a comprehensive benchmark, we also evaluate these models on widely-used conversation summarization datasets to establish strong baselines in this domain. Furthermore, we incorporate argument mining through graph construction to directly model the issues, viewpoints, and assertions present in a conversation and filter noisy input, showing comparable or improved results according to automatic and human evaluations.