arxiv: 2604.17648 · v1 · submitted 2026-04-19 · 💻 cs.CL

Recognition: unknown

ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts

Olubusayo Olabisi , Ekata Mitra , Ameeta Agrawal

Authors on Pith no claims yet

Pith reviewed 2026-05-10 05:28 UTC · model grok-4.3

classification 💻 cs.CL

keywords thread summarizationnested discourseTree of ThoughtsLLM summarizationaspect extractionatomic content unitshierarchical reasoningopinion coverage

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The pith

ThreadSumm extracts discourse aspects and atomic units then searches multiple summary candidates with Tree of Thoughts to handle nested discussion threads.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that standard large language model summarizers fail on deeply nested threads because they cannot reliably track interleaved replies, quotes, and overlapping topics. Instead, the authors build a multi-stage process that first pulls out explicit discourse aspects and Atomic Content Units, orders them to reflect the thread structure, and then uses Tree of Thoughts search to propose and refine several paragraph versions at once. A reader would care if this works because many real-world discussions on forums and social platforms contain exactly this nesting, and better summaries would preserve more distinct viewpoints and logical connections rather than flattening them into a single narrative. The method is presented as a way to turn the summarization task into an explicit hierarchical reasoning problem over interpretable units.

Core claim

ThreadSumm first extracts discourse aspects and Atomic Content Units from the nested thread, applies sentence ordering to build thread-aware sequences, and then runs Tree of Thoughts to generate and score multiple paragraph candidates that jointly optimize coherence and coverage, yielding summaries with improved logical structure, higher aspect retention, and greater opinion coverage than existing baselines.

What carries the argument

Tree of Thoughts search over paragraph candidates generated from explicit aspect and Atomic Content Unit representations, used to jointly optimize coherence and coverage.

If this is right

Summaries surface multiple viewpoints instead of collapsing to one linear strand.
Explicit aspect and content unit representations make the generated summaries more interpretable.
Iterative refinement within a single search space improves both logical flow and completeness.
The approach generalizes to any nested discourse where replies and quotes overlap.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same extraction-plus-search pattern could be tested on other hierarchically structured texts such as email chains or comment sections in news articles.
Grounding summaries in atomic units extracted upfront may reduce the risk of missing minority opinions that linear prompts often overlook.
If the ordering step proves robust, it might serve as a lightweight pre-processing stage for other discourse-level tasks.

Load-bearing premise

That large language models can extract discourse aspects and atomic content units accurately enough to capture interleaved replies and overlapping topics without introducing major errors or biases.

What would settle it

Running the method and standard LLM baselines on a held-out set of deeply nested threads and checking whether the new summaries show statistically higher scores on aspect retention and opinion coverage metrics.

Figures

Figures reproduced from arXiv: 2604.17648 by Ameeta Agrawal, Ekata Mitra, Olubusayo Olabisi.

**Figure 2.** Figure 2: Our proposed framework for Summarization of Nested Discourse Threads [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Represented vs. Not represented sentences [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Aspect Coverage of the summary relative to [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Aspect Prompt A.2 ACU Generation Prompt We use the prompt in [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗

**Figure 8.** Figure 8: Paragraph Writing Prompt A.5 Iterative Evaluation We use the prompt in [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

**Figure 6.** Figure 6: ACU Generation Prompt A.3 Sentence Ordering We use the prompt in [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 10.** Figure 10: Annotation Guidelines C LLM-as-a-judge evaluation We carried out additional analyses using LLM-as-ajudge with a different model “gpt-oss-20b”. Our results in [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗

**Figure 11.** Figure 11: Aspect Coverage of the summary relative to [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗

**Figure 12.** Figure 12: Aspect Coverage of the summary relative to [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ThreadSumm proposes a multi-stage LLM pipeline with aspect extraction, sentence ordering, and Tree of Thoughts for nested thread summarization, but the abstract gives no numbers or setup to support the claimed gains.

read the letter

The main thing to know is that this paper describes ThreadSumm as a way to handle summarization of deeply nested discussions by first pulling out discourse aspects and atomic content units, ordering sentences to reflect thread structure, and then running Tree of Thoughts to generate and refine paragraph candidates for better coherence and coverage. The abstract positions this as an improvement over standard LLM summarizers on interleaved replies and overlapping topics, with higher aspect retention and opinion coverage as the payoff. That framing is straightforward and targets a genuine pain point in conversation summarization. The integration of explicit content planning with ToT search for joint optimization looks like the concrete new piece, since prior work on thread summarization has not combined these steps in quite this way for nested structures. The design also keeps the intermediate representations interpretable, which is a practical plus for debugging or extending the system in social media or forum settings. The paper does a decent job spelling out why linear summarizers miss multiple viewpoints and how the ordering step tries to surface them. That said, the central claims rest on unshown experiments. The abstract asserts better logical structure and retention without metrics, baselines, datasets, or even prompt details, so the soundness is thin and the stress-test point about extraction errors propagating through the pipeline lands directly. Without human validation or ablation results, it is difficult to know whether the later stages actually recover from early mistakes or just paper over them. No derivations or fitted parameters appear, which fits an applied engineering paper, but the lack of any quantitative grounding makes it hard to judge real progress. This is for NLP researchers working on multi-party or discourse-aware summarization who want ideas for structuring LLM calls on complex inputs. A reader could pull the pipeline design and adapt pieces even if the evaluation needs work. It deserves peer review so the full experiments and comparisons can be checked properly rather than desk-rejected on the abstract alone.

Referee Report

3 major / 1 minor

Summary. The paper introduces ThreadSumm, a multi-stage LLM framework for summarizing deeply nested discussion threads. It performs LLM-based extraction of discourse aspects and Atomic Content Units, applies sentence ordering to build thread-aware sequences, and employs Tree of Thoughts search to generate and iteratively refine multiple paragraph candidates, jointly optimizing for coherence and coverage. The authors claim this yields improved logical structure, higher aspect retention, and better opinion coverage compared to existing baselines.

Significance. If the empirical results hold, the work provides a structured, interpretable alternative to direct LLM summarization for complex online discourse, addressing challenges like interleaved replies and multi-viewpoint coverage through explicit content planning and search-based refinement. This could be relevant for applications in forum and social media analysis, though the lack of visible experimental details limits its assessed contribution to the field.

major comments (3)

[Abstract] Abstract: The central claim of 'improved performance in generating logically structured summaries' and 'higher aspect retention and opinion coverage' is presented without any quantitative metrics (e.g., ROUGE, human evaluation scores), baseline descriptions, dataset details, or experimental setup, leaving the primary contribution unsupported by evidence.
[Framework description] Framework description (content planning stage): The LLM-based extraction of discourse aspects and Atomic Content Units, followed by sentence ordering, is described at a high level but provides no information on prompt design, few-shot examples, or validation (e.g., inter-annotator agreement or error analysis). Since errors in this foundational step would directly propagate into the Tree of Thoughts search space and undermine later refinement, this is load-bearing for the reliability claims.
[Tree of Thoughts component] Tree of Thoughts component: The scoring mechanism for evaluating paragraph candidates (jointly optimizing coherence and coverage) is not specified, including any details on the search algorithm, heuristics, or how multiple proposals are compared, making it impossible to assess whether the multi-proposal design actually delivers the claimed improvements.

minor comments (1)

[Abstract] The abstract and introduction could more explicitly define 'Atomic Content Units' and 'thread-aware sequences' on first use to improve readability for readers unfamiliar with the specific terminology.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below and will revise the paper to incorporate additional details and quantitative support as outlined.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim of 'improved performance in generating logically structured summaries' and 'higher aspect retention and opinion coverage' is presented without any quantitative metrics (e.g., ROUGE, human evaluation scores), baseline descriptions, dataset details, or experimental setup, leaving the primary contribution unsupported by evidence.

Authors: We agree that the abstract would be strengthened by including key quantitative results. The full manuscript reports experimental comparisons against baselines on nested discussion datasets, with metrics including ROUGE scores for coherence and human evaluations for aspect retention and opinion coverage. In the revised version, we will update the abstract to concisely report these specific improvements and experimental details. revision: yes
Referee: [Framework description] Framework description (content planning stage): The LLM-based extraction of discourse aspects and Atomic Content Units, followed by sentence ordering, is described at a high level but provides no information on prompt design, few-shot examples, or validation (e.g., inter-annotator agreement or error analysis). Since errors in this foundational step would directly propagate into the Tree of Thoughts search space and undermine later refinement, this is load-bearing for the reliability claims.

Authors: We acknowledge that the content planning stage requires more implementation details to support reproducibility and address potential error propagation. We will expand this section (and add an appendix if needed) with the exact prompt templates, few-shot examples, and validation steps such as manual error analysis or agreement metrics used for aspect and Atomic Content Unit extraction. revision: yes
Referee: [Tree of Thoughts component] Tree of Thoughts component: The scoring mechanism for evaluating paragraph candidates (jointly optimizing coherence and coverage) is not specified, including any details on the search algorithm, heuristics, or how multiple proposals are compared, making it impossible to assess whether the multi-proposal design actually delivers the claimed improvements.

Authors: We will revise the Tree of Thoughts section to fully specify the scoring mechanism, including how coherence and coverage are jointly evaluated (via LLM-based judges or defined heuristics), the search algorithm (e.g., branching factor, depth limits, and selection criteria), and the comparison process among paragraph candidates. This will allow readers to assess the multi-proposal refinement approach. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper presents a descriptive multi-stage LLM engineering framework for thread summarization with no mathematical derivations, equations, fitted parameters, or self-referential predictions. Content planning via aspect/ACU extraction and sentence ordering, followed by Tree of Thoughts search, relies on external LLM capabilities rather than any internal derivation that reduces to its own inputs by construction. Claims of improved performance are supported by empirical comparisons to baselines, with no load-bearing steps equivalent to the inputs. This is the expected honest non-finding for an applied NLP system description.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The framework rests on standard assumptions about LLM capabilities for extraction and reasoning tasks, with no free parameters, new axioms, or invented entities introduced.

pith-pipeline@v0.9.0 · 5441 in / 1037 out tokens · 43618 ms · 2026-05-10T05:28:52.776811+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

16 extracted references · 4 canonical work pages · 1 internal anchor

[1]

BooookScore : A systematic exploration of book-length summarization in the era of LLMs

Booookscore: A systematic exploration of book-length summarization in the era of llms.arXiv preprint arXiv:2310.00785. Alexander Richard Fabbri, Faiaz Rahman, Imad Rizvi, Borui Wang, Haoran Li, Yashar Mehdad, and Dragomir Radev. 2021. Convosumm: Conversation summarization benchmark and improved abstractive summarization with argument mining. InProceed- in...

work page arXiv 2021
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Large Language Model based Multi-Agents: A Survey of Progress and Challenges

Using bert encoding and sentence-level lan- guage model for sentence ordering. InInternational Conference on Text, Speech, and Dialogue, pages 318–330. Springer. Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V Chawla, Olaf Wiest, and Xi- angliang Zhang. 2024. Large language model based multi-agents: A survey of progress and chall...

work page internal anchor Pith review arXiv 2024
[3]

Amy Zhang, Bryan Culbertson, and Praveen Paritosh

Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822. Amy Zhang, Bryan Culbertson, and Praveen Paritosh
[4]

Inproceedings of the interna- tional AAAI conference on web and social media, volume 11, pages 357–366

Characterizing online discussion using coarse discourse sequences. Inproceedings of the interna- tional AAAI conference on web and social media, volume 11, pages 357–366. Shiyue Zhang, Asli Celikyilmaz, Jianfeng Gao, and Mohit Bansal. 2021. Emailsum: Abstractive email thread summarization.arXiv preprint arXiv:2107.14691. Yusen Zhang, Ruoxi Sun, Yanfei Che...

work page arXiv 2021
[5]

Read the document carefully
[6]

Read the Aspect list carefully
[7]

For each aspect, identify all distinct fac- tual claims, propositions, or ideas
[8]

Each ACU should express only one idea
[9]

Each ACU should be independent of surrounding text
[10]

Each ACU should be written in clear and concise language
[11]

Rewrite each idea as a minimal, stan- dalone statement
[12]

Do not include reasoning steps or expla- nations, only the extracted statements
[13]

Output the ACUs in a list, where each item is one ACU string
[14]

Figure 6: ACU Generation Prompt A.3 Sentence Ordering We use the prompt in Figure 7 to reorder the ACUs generated in the previous sentence to form a coher- ent flow

Return only the file as output. Figure 6: ACU Generation Prompt A.3 Sentence Ordering We use the prompt in Figure 7 to reorder the ACUs generated in the previous sentence to form a coher- ent flow. Sentence Ordering Prompt You are an expert at reordering documents for them to follow a logical and coherent flow. Ensure every sentence appears ex- actly once...
[15]

Coherence (logical flow, readability, and overall structure, ranging from 0.0 to 1.0)
[16]

gpt-oss-20b

Coverage (how completely it includes all key ideas/sentences from the original text, ranging from 0.0 to 1.0) Return the two scores as two numbers separated by a space (e.g., ’0.9 1.0’). If the paragraph con- tains significantly fewer or more sentences than the original text, or if it changes the core meaning, score coverage lower. Do not provide any othe...

work page arXiv