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A Neural Attention Model for Abstractive Sentence Summarization
Open accessAbstract
Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build.In this work, we propose a fully data-driven approach to abstractive sentence summarization.Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence.While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data.The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.
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RefSum: Refactoring Neural Summarization
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How Large Language Models are Transforming Machine-Paraphrase Plagiarism
Wahle, J. P., Ruas, T., Kirstein, F., & Gipp, B. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.62 · Cited by 35
“Machine-paraphrasing experiences a growing research interest from NLP for learning semantic representations and related applications (Rush et al., 2015; McCann et al., 2018).”
BackgroundExplainaBoard: An Explainable Leaderboard for NLP
Liu, P., Fu, J., Xiao, Y., Yuan, W., Chang, S., Dai, J., Liu, Y., Ye, Z., & Neubig, G. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations · doi:10.18653/v1/2021.acl-demo.34 · Cited by 35
Differentiable Scheduled Sampling for Credit Assignment
Goyal, K., Dyer, C., & Berg-Kirkpatrick, T. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2058 · Cited by 34
Correlating Automated and Human Evaluation of Code Documentation Generation Quality
Hu, X., Chen, Q., Wang, H., Xia, X., Lo, D., & Zimmermann, T. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3502853 · Cited by 33
“A theme-rewriting approach for generating algebra word problems,”
Koncel-Kedziorski, R., Konstas, I., Zettlemoyer, L., & Hajishirzi, H. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1168 · Cited by 30
Automatic Product Copywriting for E-commerce
Zhang, X., Zou, Y., Zhang, H., Zhou, J., Diao, S., Chen, J., Ding, Z., He, Z., He, X., Xiao, Y., et al. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21508 · Cited by 24
QuickEdit: Editing Text and Translations by Crossing Words Out
Grangier, D., & Auli, M. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1025 · Cited by 20
(2022) · doi:10.2139/ssrn.4496380 · Cited by 20
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation
Deng, Z., Peng, H., Xia, C., Li, J., He, L., & Yu, P. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.555 · Cited by 19
Wait, But Why?: Assessing Behavior Explanation Strategies for Real-Time Strategy Games
Robertson, J., Kokkinakis, A. V., Hook, J., Kirman, B., Block, F., Ursu, M. F., Patra, S., Demediuk, S., Drachen, A., & Olarewaju, O. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450699 · Cited by 18
Metadata
- Authors
- Rush, A. M., Chopra, S., & Weston, J.
- Venue
- Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
- Published
- 2015
- Pages
- 379-389
- OpenAlex
- W1843891098
- Cited by
- 2,656
- References
- 5
- FWCI
- 126.89
- Type
- conference paper
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
