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Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge
Open accessAbstract
Citation texts are sometimes not very informative or in some cases inaccurate by themselves; they need the appropriate context from the referenced paper to reflect its exact contributions. To address this problem, we propose an unsupervised model that uses distributed representation of words as well as domain knowledge to extract the appropriate context from the reference paper. Evaluation results show the effectiveness of our model by significantly outperforming the state-of-the-art. We furthermore demonstrate how an effective contextualization method results in improving citation-based summarization of the scientific articles.
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Researchers have investigated methods of finding cited spans in the cited papers, including feature-based methods, domain-specific knowledge, and a CNN-based model for joint prediction of cited spans and citation function.
“Researchers have also investigated methods of finding cited spans in the cited papers. Examples include feature-based methods (Cohan et al., 2015), domain-specific knowledge (Cohan and Goharian, 2017), and a recent CNNbased model for joint prediction of cited spans and citation function (Su et al., 2018).”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Cohan and Goharian, 2017
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Research works printed but not linked: 21
- Amjad Abu-Jbara and Dragomir Radev 2012. Reference scope identification in citing sentences NAACL-HLT. ACL 80--90. Amjad Abu-Jbara and Dragomir Radev 2012. Reference scope identification in citing sentences NAACL-HLT. ACL 80--90.
- 10.18653/v1/D15-1045 · DOI
- Cohan Arman, 2017, International Journal on Digital Libraries
- 10.3115/v1/N15-1110 · DOI
- de Waard Anita, 2012, Workshop on Detecting Structure in Scholarly Discourse. ACL, 47--55
- 10.3115/v1/N15-1184 · DOI
- Ganguly Debasis, 2015, Jones
- Hill Felix, 2015, Simlex-999: Evaluating semantic models with similarity estimation. Computational Linguistics
- Jaidka Kokil, 2016, Sajal Rustagi, and Min-Yen Kan
- Jian Fanghong, 2016, Jiashu Zhao, Tingting He, and Po Hu.
- Mei Qiaozhu, 2008, ACL, 8
- Mitra Bhaskar, 2016, A dual embedding space model for document ranking. CoRR arXiv:1602.01137 · arXiv
- Nallapati Ramesh, 2016, Classify or Select: Neural Architectures for Extractive Document Summarization. arXiv:1611.04244 · arXiv
- 10.1007/978-1-4614-3223-4_3 · DOI
- Qazvinian Vahed, 2008, Radev
- Anna Ritchie Stephen Robertson and Simone Teufel. 2008. Comparing Citation Contexts for Information Retrieval CIKM. ACM 213--222. Anna Ritchie Stephen Robertson and Simone Teufel. 2008. Comparing Citation Contexts for Information Retrieval CIKM. ACM 213--222. · DOI · Paper page
- Ágnes Sándor and Anita De Waard 2012. Identifying claimed knowledge updates in biomedical research articles Proceedings of the Workshop on Detecting Structure in Scholarly Discourse. ACL 10--17. Ágnes Sándor and Anita De Waard 2012. Identifying claimed knowledge updates in biomedical research articles Proceedings of the Workshop on Detecting Structure in Scholarly Discourse. ACL 10--17.
- 10.1162/089120102762671936 · DOI
- Ivan Vulić and Marie-Francine Moens 2015. Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings SIGIR. Ivan Vulić and Marie-Francine Moens 2015. Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings SIGIR. · DOI
- Stephen Wan Cécile Paris and Robert Dale. 2009. Whetting the appetite of scientists: Producing summaries tailored to the citation context JCDL. 59--68. Stephen Wan Cécile Paris and Robert Dale. 2009. Whetting the appetite of scientists: Producing summaries tailored to the citation context JCDL. 59--68. · DOI
- 10.1145/984321.984322 · DOI
Cited by
Sorted by citations
Structural Scaffolds for Citation Intent Classification in Scientific Publications
Cohan, A., Ammar, W., van Zuylen, M., & Cady, F. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1361 · Cited by 214
Self-citationRAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
Sarthi, P., Abdullah, S., Tuli, A., Khanna, S., Goldie, A., & Manning, C. D. (2024) · arXiv · doi:10.48550/arxiv.2401.18059 · Cited by 36
Automatic Related Work Section in Scientific Article: Research Trends and Future Directions
Justitia, A., & Wang, H.-C. (2022) · 2022 International Seminar on Intelligent Technology and Its Applications (ISITIA) · doi:10.1109/isitia56226.2022.9855331 · Cited by 2
Metadata
- Authors
- Cohan, A., & Goharian, N.
- Affiliations
- Georgetown University (US)
- ORCID
- Cohan, Arman
- Venue
- Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2017-08-07
- Pages
- 1133-1136
- OpenAlex
- W2619209709
- Cited by
- 23
- References
- 0
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 2 · 2020: 3 · 2019: 5 · 2018: 2 · 2017: 1
- FWCI
- 1.37
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Keywords
- word embeddings, domain knowledge, unsupervised learning, distributed word representations
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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.
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