The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Introduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Moses: open source toolkit for statistical machine translation
Koehn, P., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., et al. (2007) · Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions - ACL '07 · doi:10.3115/1557769.1557821 · Cited by 4,735
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317
Cited by
Items 101–104 of 104
Learning to summarize and answer questions about a virtual robot’s past actions
DeChant, C., Akinola, I., & Bauer, D. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10134-4 · Cited by 14
Caption Alignment and Structure-Aware Attention for Scientific Table-to-Text Generation
Wu, J., Karlsson, B. F., & Okumura, M. (2024) · IEEE Access · doi:10.1109/access.2024.3507772 · Cited by 1
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization
Zhong, M., Yin, D., Yu, T., Zaidi, A., Mutuma, M., Jha, R., Awadallah, A. H., Celikyilmaz, A., Liu, Y., Qiu, X., et al. (2021) · arXiv · doi:10.48550/arxiv.2104.05938
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.
