Details
| Original language | English |
|---|---|
| Title of host publication | CIKM 2022 |
| Subtitle of host publication | Proceedings of the 31st ACM International Conference on Information and Knowledge Management |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 1552-1562 |
| Number of pages | 11 |
| ISBN (electronic) | 9781450392365 |
| Publication status | Published - 17 Oct 2022 |
| Event | 31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States Duration: 17 Oct 2022 → 21 Oct 2022 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
|---|
Abstract
Recent fashion of information propagation on Twitter makes the platform a crucial conduit for tactical data and emergency responses during disasters. However, the real-time information about crises is immersed in a large volume of emotional and irrelevant posts. It brings the necessity to develop an automatic tool to identify disaster-related messages and summarize the information for data consumption and situation planning. Besides, explainability of the methods is crucial in determining their applicability in real-life scenarios. Recent studies also highlight the importance of learning a good latent representation of tweets for several downstream tasks. In this paper, we take advantage of state-of-the-art methods, such as transformers and contrastive learning to build an interpretable classifier. Our proposed model classifies Twitter messages into different humanitarian categories and also extracts rationale snippets as supporting evidence for output decisions. The contrastive learning framework helps to learn better representations of tweets by bringing the related tweets closer in the embedding space. Furthermore, we employ classification labels and rationales to efficiently generate summaries of crisis events. Extensive experiments over different crisis datasets show that (i). our classifier obtains the best performance-interpretability trade-off, (ii). the proposed summarizer shows superior performance (1.4%-22% improvement) with significantly less computation cost than baseline models.
Keywords
- classification, contrastive learning, crisis events, interpretability, summarization
ASJC Scopus subject areas
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CIKM 2022 : Proceedings of the 31st ACM International Conference on Information and Knowledge Management. Association for Computing Machinery (ACM), 2022. p. 1552-1562 (International Conference on Information and Knowledge Management, Proceedings).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Rationale Aware Contrastive Learning Based Approach to Classify and Summarize Crisis-Related Microblogs
AU - Nguyen, Thi Huyen
AU - Rudra, Koustav
N1 - Funding Information: This work was partially funded by the DFG Grant NI-1760/1-1, and the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101021866.
PY - 2022/10/17
Y1 - 2022/10/17
N2 - Recent fashion of information propagation on Twitter makes the platform a crucial conduit for tactical data and emergency responses during disasters. However, the real-time information about crises is immersed in a large volume of emotional and irrelevant posts. It brings the necessity to develop an automatic tool to identify disaster-related messages and summarize the information for data consumption and situation planning. Besides, explainability of the methods is crucial in determining their applicability in real-life scenarios. Recent studies also highlight the importance of learning a good latent representation of tweets for several downstream tasks. In this paper, we take advantage of state-of-the-art methods, such as transformers and contrastive learning to build an interpretable classifier. Our proposed model classifies Twitter messages into different humanitarian categories and also extracts rationale snippets as supporting evidence for output decisions. The contrastive learning framework helps to learn better representations of tweets by bringing the related tweets closer in the embedding space. Furthermore, we employ classification labels and rationales to efficiently generate summaries of crisis events. Extensive experiments over different crisis datasets show that (i). our classifier obtains the best performance-interpretability trade-off, (ii). the proposed summarizer shows superior performance (1.4%-22% improvement) with significantly less computation cost than baseline models.
AB - Recent fashion of information propagation on Twitter makes the platform a crucial conduit for tactical data and emergency responses during disasters. However, the real-time information about crises is immersed in a large volume of emotional and irrelevant posts. It brings the necessity to develop an automatic tool to identify disaster-related messages and summarize the information for data consumption and situation planning. Besides, explainability of the methods is crucial in determining their applicability in real-life scenarios. Recent studies also highlight the importance of learning a good latent representation of tweets for several downstream tasks. In this paper, we take advantage of state-of-the-art methods, such as transformers and contrastive learning to build an interpretable classifier. Our proposed model classifies Twitter messages into different humanitarian categories and also extracts rationale snippets as supporting evidence for output decisions. The contrastive learning framework helps to learn better representations of tweets by bringing the related tweets closer in the embedding space. Furthermore, we employ classification labels and rationales to efficiently generate summaries of crisis events. Extensive experiments over different crisis datasets show that (i). our classifier obtains the best performance-interpretability trade-off, (ii). the proposed summarizer shows superior performance (1.4%-22% improvement) with significantly less computation cost than baseline models.
KW - classification
KW - contrastive learning
KW - crisis events
KW - interpretability
KW - summarization
UR - http://www.scopus.com/inward/record.url?scp=85140823314&partnerID=8YFLogxK
U2 - 10.1145/3511808.3557426
DO - 10.1145/3511808.3557426
M3 - Conference contribution
AN - SCOPUS:85140823314
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1552
EP - 1562
BT - CIKM 2022
PB - Association for Computing Machinery (ACM)
T2 - 31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Y2 - 17 October 2022 through 21 October 2022
ER -