Indicative Summarization of Long Discussions
Shahbaz Syed Dominik Schwabe Khalid Al-Khatib Martin Potthast
Online forums encourage the exchange and discussion of different stances on
many topics. Not only do they provide an opportunity to present one's own
arguments, but may also gather a broad cross-section of others' arguments.
However, the resulting long discussions are difficult to overview. This paper
presents a novel unsupervised approach using large language models (LLMs) to
generating indicative summaries for long discussions that basically serve as
tables of contents. Our approach first clusters argument sentences, generates
cluster labels as abstractive summaries, and classifies the generated cluster
labels into argumentation frames resulting in a two-level summary. Based on an
extensively optimized prompt engineering approach, we evaluate 19~LLMs for
generative cluster labeling and frame classification. To evaluate the
usefulness of our indicative summaries, we conduct a purpose-driven user study
via a new visual interface called Discussion Explorer: It shows that our
proposed indicative summaries serve as a convenient navigation tool to explore
long discussions.