Pith. sign in

REVIEW 4 cited by

DialogSum: A Real-Life Scenario Dialogue Summarization Dataset

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2105.06762 v4 pith:7FGMUAEA submitted 2021-05-14 cs.CL cs.AI

DialogSum: A Real-Life Scenario Dialogue Summarization Dataset

Yulong Chen , Yang Liu , Liang Chen , Yue Zhang This is my paper
classification cs.CL cs.AI
keywords summarizationdialoguedialogsumdatasetdeeplarge-scalelearningneural
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

Proposal of large-scale datasets has facilitated research on deep neural models for news summarization. Deep learning can also be potentially useful for spoken dialogue summarization, which can benefit a range of real-life scenarios including customer service management and medication tracking. To this end, we propose DialogSum, a large-scale labeled dialogue summarization dataset. We conduct empirical analysis on DialogSum using state-of-the-art neural summarizers. Experimental results show unique challenges in dialogue summarization, such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense, which require specific representation learning technologies to better deal with.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws

    cs.LG 2026-04 unverdicted novelty 6.0

    Emergent intelligence is recast as the existence of the limit of performance E(N,P,K) as N,P,K to infinity, with necessary and sufficient conditions derived via nonlinear Lipschitz operator theory and scaling laws obt...

  2. Learning to Control Summaries with Score Ranking

    cs.CL 2026-04 unverdicted novelty 6.0

    A score-ranking loss enables controllable summarization by aligning outputs to evaluation scores, matching SOTA performance with dimension-specific control on LLaMA, Qwen, and Mistral.

  3. A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws

    cs.LG 2026-04 unverdicted novelty 5.0

    Emergent intelligence corresponds to the limit of a performance function E(N,P,K) as N, P, K go to infinity, originating from a parameter-limit architecture whose existence is governed by Lipschitz conditions, with sc...

  4. A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws

    cs.LG 2026-04 unverdicted novelty 3.0

    Formalizes emergent intelligence in foundation models as the limit of E(N,P,K) as N,P,K approach infinity, proves existence conditions via nonlinear Lipschitz operators, and derives scaling laws from covering numbers.