Papers with coherence modeling
SNaC: Coherence Error Detection for Narrative Summarization (2022.emnlp-main)
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| Challenge: | SNaC framework is used to evaluate long summaries, but it fails to identify gaps in coherence . nallapati and colleagues have developed a framework for fine-grained annotations of long summarizations . |
| Approach: | They propose a narrative coherence evaluation framework for fine-grained annotations of long summaries that can be used to evaluate coherent narratives. |
| Outcome: | The proposed framework can support future work in document summarization and coherence evaluation, the authors show . |
Jigsaw Pieces of Meaning: Modeling Discourse Coherence with Informed Negative Sample Synthesis (2024.findings-eacl)
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| Challenge: | Existing work on creating “informed” incoherent samples for coherence modeling has focused on permutations of a coherent document . |
| Approach: | They propose to use Constituency trees, Part-of-speech, semantic overlap to create “informed” negative samples that better represent or mimic incoherence. |
| Outcome: | The proposed methods improve the quality of the negative sample. |
On the Creation of a Corpus for Coherence Evaluation of Discursive Units (2020.lrec-1)
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| Challenge: | Most corpora for textual coherence evaluation are composed of randomly shuffled sentences that focus on sentence ordering. |
| Approach: | They propose to use a variety of corruption strategies to build a corpus of incoherent pairs of sentences by swapping their discourse connective or a discourse argument. |
| Outcome: | The proposed corpus is constructed from discourse argument pairs from the Penn Discourse Tree Bank and is compared with existing corpus models. |
Evaluating Text Coherence at Sentence and Paragraph Levels (2020.lrec-1)
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| Challenge: | Existing text ordering models have been used to test coherence in NLP for a long time. |
| Approach: | They propose to perform paragraph ordering task and sentence ordering by using four corpora from different domains. |
| Outcome: | The proposed model performs better under certain extreme conditions than the most prevalent metric used before. |
A Unified Neural Coherence Model (D19-1)
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| Challenge: | Existing models for coherence modeling fail on harder tasks with more realistic application scenarios. |
| Approach: | They propose a unified coherence model that incorporates sentence grammar, inter-sentence coherent relations, and global coherency patterns into a common neural framework. |
| Outcome: | The proposed model outperforms existing models on local and global discrimination tasks and outperformed existing models by a good margin. |
Discourse Relation-Enhanced Neural Coherence Modeling (2025.acl-long)
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| Challenge: | Existing work on coherence modeling has focused on integrating entity-based models. |
| Approach: | They propose a model that integrates text- and relation-based features for coherence assessment using position-aware attention and a visible matrix. |
| Outcome: | The proposed model improves baselines on two benchmarks and shows that relation features are important for coherence modeling. |
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks (2021.eacl-main)
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| Challenge: | Coherence models are typically evaluated only on synthetic tasks, which may not be representative of their performance in downstream applications. |
| Approach: | They compare models' performance on synthetic sentences with those on retrieval-based dialog. |
| Outcome: | The proposed models perform poorly on synthetic sentences and retrieval-based dialog tasks. |
Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks (2023.acl-long)
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| Challenge: | Existing methods focus on a single document’s coherence patterns, ignoring the underlying correlation between documents. |
| Approach: | They propose a GCN-based coherence model that captures structural similarities between documents by mining subgraph patterns and a heterogeneous graph for the training corpus. |
| Outcome: | The proposed model outperforms baseline models on discourse coherence and automated essay scoring tasks. |
BERT-enhanced Relational Sentence Ordering Network (2020.emnlp-main)
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| Challenge: | Existing approaches to improve coherence modeling for paragraphs have been developed. |
| Approach: | They propose a BERT-enhanced Relational Sentence Ordering Network to capture better dependency relationship among sentences and exploit it with a deep relational module. |
| Outcome: | The proposed model shows significant improvement over the state-of-the-art on six datasets. |
Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis (2024.lrec-main)
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| Challenge: | Aspect-category-based sentiment analysis (ACSA) is a popular approach for identifying aspect categories and predicting their sentiments. |
| Approach: | They propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) to capture contexts across the whole review and to help the implicit aspect and sentiment identification. |
| Outcome: | The proposed network decouples multiple aspects and sentiment features and achieves state-of-the-art (SOTA) performance. |
Incremental Neural Lexical Coherence Modeling (2020.coling-main)
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| Challenge: | Recent work on pretrained language models has led to significant improvements in a range of NLP tasks. |
| Approach: | They propose a coherence model which interprets sentences incrementally to capture lexical relations between them. |
| Outcome: | The proposed model interprets sentences incrementally to capture lexical relations between them. |
Joint Modeling of Entities and Discourse Relations for Coherence Assessment (2025.emnlp-main)
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| Challenge: | Existing work on coherence modeling focuses on entity features or discourse relation features, with little attention given to combining the two. |
| Approach: | They propose two methods for jointly modeling entities and discourse relations for coherence assessment. |
| Outcome: | The proposed methods significantly improve the performance of coherence models on three benchmark datasets. |