TY - GEN
T1 - BACO
T2 - Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021
AU - Ge, Yubin
AU - Dinh, Ly
AU - Liu, Xiaofeng
AU - Su, Jinsong
AU - Lu, Ziyao
AU - Wang, Ante
AU - Diesner, Jana
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics
PY - 2021
Y1 - 2021
N2 - In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper. We present BACO, a BAckground knowledge- and COntent-based framework for citing sentence generation, which considers two types of information: (1) background knowledge by leveraging structural information from a citation network; and (2) content, which represents in-depth information about what to cite and why to cite. First, a citation network is encoded to provide background knowledge. Second, we apply salience estimation to identify what to cite by estimating the importance of sentences in the cited paper. During the decoding stage, both types of information are combined to facilitate the text generation. We then conduct joint training of the generator and citation function classification to make the model aware of why to cite. Our experimental results show that our framework outperforms comparative baselines.
AB - In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper. We present BACO, a BAckground knowledge- and COntent-based framework for citing sentence generation, which considers two types of information: (1) background knowledge by leveraging structural information from a citation network; and (2) content, which represents in-depth information about what to cite and why to cite. First, a citation network is encoded to provide background knowledge. Second, we apply salience estimation to identify what to cite by estimating the importance of sentences in the cited paper. During the decoding stage, both types of information are combined to facilitate the text generation. We then conduct joint training of the generator and citation function classification to make the model aware of why to cite. Our experimental results show that our framework outperforms comparative baselines.
UR - http://www.scopus.com/inward/record.url?scp=85118935151&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118935151&partnerID=8YFLogxK
U2 - 10.18653/v1/2021.acl-long.116
DO - 10.18653/v1/2021.acl-long.116
M3 - Conference contribution
AN - SCOPUS:85118935151
T3 - ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference
SP - 1466
EP - 1478
BT - ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference
PB - Association for Computational Linguistics (ACL)
Y2 - 1 August 2021 through 6 August 2021
ER -