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ARXIV:2604.02451 · NLP COHERENCE MODELING · SUBMITTED 06 APR · 20:18 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02451NLP COHERENCE MODELINGSUBMITTED 06 APR · 20:18 UTCFRESHNESS UNKNOWNNishit Asnani · Rohan Badlani · arXiv
This research explores using sentence skeletons to model narrative coherence, finding that sentence-level analysis remains superior.
Opportunity summary
Pain This research explores using sentence skeletons to model narrative coherence, finding that sentence-level analysis remains superior.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
This research explores using sentence skeletons to model narrative coherence, finding that sentence-level analysis remains superior. It has applications in detecting incoherent structures and helping the author fix them.
Modeling coherence in text has been a task that has excited NLP researchers since a long time. It has applications in detecting incoherent structures and helping the author fix them.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We propose a new Sentence/Skeleton Similarity Network (SSN) for modeling coherence across pairs of sentences, and show that this network performs much better than…
NLP Coherence Modeling moved forward this cycle; last verified April 2026. Public score 3.0/10.
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This research explores using sentence skeletons to model narrative coherence, finding that sentence-level analysis remains superior.
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10.48550/arXiv.2604.02451This research explores using sentence skeletons to model narrative coherence, finding that sentence-level analysis remains superior.
Abstract
Modeling coherence in text has been a task that has excited NLP researchers since a long time. It has applications in detecting incoherent structures and helping the author fix them. There has been recent work in using neural networks to extract a skeleton from one sentence, and then use that skeleton to generate the next sentence for coherent narrative story generation. In this project, we aim to study if the consistency of skeletons across subsequent sentences is a good metric to characterize the coherence of a given body of text. We propose a new Sentence/Skeleton Similarity Network (SSN) for modeling coherence across pairs of sentences, and show that this network performs much better than baseline similarity techniques like cosine similarity and Euclidean distance. Although skeletons appear to be promising candidates for modeling coherence, our results show that sentence-level models outperform those on skeletons for evaluating textual coherence, thus indicating that the current state-of-the-art coherence modeling techniques are going in the right direction by dealing with sentences rather than their sub-parts.
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PROBLEM
This research explores using sentence skeletons to model narrative coherence, finding that sentence-level analysis remains superior. It has applications in detecting incoherent structures and helping the author fix them.
METHOD
Modeling coherence in text has been a task that has excited NLP researchers since a long time. It has applications in detecting incoherent structures and helping the author fix them.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We propose a new Sentence/Skeleton Similarity Network (SSN) for modeling coherence across pairs of sentences, and show that this network performs much better than baseline similarity techniques like cosin...
WHY NOW
NLP Coherence Modeling moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This research explores using sentence skeletons to model narrative coherence, finding that sentence-level analysis remains superior. It has applications in detecting incoherent structures and helping the author fix them.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Modeling coherence in text has been a task that has excited NLP researchers since a long time. It has applications in detecting incoherent structures and helping the author fix them.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We propose a new Sentence/Skeleton Similarity Network (SSN) for modeling coherence across pairs of sentences, and show that this network performs much better than baseline similarity techniques like cosine similarity and Euclidean distance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
NLP Coherence Modeling moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This research explores using sentence skeletons to model narrative coherence, finding that sentence-level analysis remains superior.
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NLP Coherence Modeling
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