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 -