  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.
