The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Modeling Local Coherence: An Entity-Based Approach
Open accessAbstract
This article proposes a novel framework for representing and measuring local coherence. Central to this approach is the entity-grid representation of discourse, which captures patterns of entity distribution in a text. The algorithm introduced in the article automatically abstracts a text into a set of entity transition sequences and records distributional, syntactic, and referential information about discourse entities. We re-conceptualize coherence assessment as a learning task and show that our entity-based representation is well-suited for ranking-based generation and text classification tasks. Using the proposed representation, we achieve good performance on text ordering, summary coherence evaluation, and readability assessment.
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MANN, W. C., & THOMPSON, S. A. (1988) · Text - Interdisciplinary Journal for the Study of Discourse · doi:10.1515/text.1.1988.8.3.243 · Cited by 3,853
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Cited by
A Hierarchical Neural Autoencoder for Paragraphs and Documents
Li, J., Luong, T., & Jurafsky, D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1107 · Cited by 490
Revisiting readability: a unified framework for predicting text quality
Pitler, E., & Nenkova, A. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613742 · Cited by 375
Automated Essay Scoring: A Survey of the State of the Art
Ke, Z., & Ng, V. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/879 · Cited by 247
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
Sharma, E., Li, C., & Wang, L. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1212 · Cited by 173
Do Massively Pretrained Language Models Make Better Storytellers?
See, A., Pappu, A., Saxena, R., Yerukola, A., & Manning, C. D. (2019) · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) · doi:10.18653/v1/k19-1079 · Cited by 139
Metadata
- Authors
- Barzilay, R., & Lapata, M.
- Venue
- Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2008-03
- Volume
- 34
- Issue
- 1
- Pages
- 1-34
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W2140676672
- Cited by
- 671
- References
- 2
- FWCI
- 49.29
- Type
- journal article
- Language
- en
- Open access
- bronze
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