ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts
Summarizing deeply nested discussion threads requires handling interleaved replies, quotes, and overlapping topics, which standard LLM summarizers struggle to capture reliably. We introduce ThreadSumm, a multi-stage LLM framework that treats thread summarization as a hierarchical reasoning problem over explicit aspect and content unit representations. Our method first performs content planning via LLM-based extraction of discourse aspects and Atomic Content Units, then applies sentence ordering to construct thread-aware sequences that surface multiple viewpoints rather than a single linear strand. On top of these interpretable units, ThreadSumm employs a Tree of Thoughts search that generates and scores multiple paragraph candidates, jointly optimizing coherence and coverage within a unified search space. With this multi-proposal and iterative refinement design, we show improved performance in generating logically structured summaries compared to existing baselines, while achieving higher aspect retention and opinion coverage in nested discussions.
Code (0)
등록된 구현이 없습니다.
Tasks
Sentence OrderingSimilar Papers 제목 키워드 기반
Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer
Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. However, constructing a coherent and informative summary is difficult using a pre-traine…
Extractive Text SummarizationSentencePredicting Discourse Trees from Transformer-based Neural Summarizers
Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees …
Discourse ParsingSingle Document Summarization based on Nested Tree Structure
Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs
With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred. This paper delves into the inquiry of identifying discernible and unique linguis…
Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. H…
General ClassificationRumour DetectionStance Classification