TY - GEN

T1 - Towards an Interpretable Approach to Classify and Summarize Crisis Events from Microblogs

AU - Nguyen, Thi Huyen

AU - Rudra, Koustav

N1 - Funding Information: This work was partially funded by the DFG Grant NI-1760/1-1 Funding Information: This material is based upon work supported by ONR (N00014-21-1-4002), and the U.S. Department of Homeland Security under Grant Award Number 17STQAC00001-05-003. Kai Shu is supported by the NSF award #2109316.

PY - 2022/4/25

Y1 - 2022/4/25

N2 - Microblogging platforms like Twitter have been heavily leveraged to report and exchange information about natural disasters. The real-time data on these sites is highly helpful in gaining situational awareness and planning aid efforts. However, disaster-related messages are immersed in a high volume of irrelevant information. The situational data of disaster events also vary greatly in terms of information types ranging from general situational awareness (caution, infrastructure damage, casualties) to individual needs or not related to the crisis. It thus requires efficient methods to handle data overload and prioritize various types of information. This paper proposes an interpretable classification-summarization framework that first classifies tweets into different disaster-related categories and then summarizes those tweets. Unlike existing work, our classification model can provide explanations or rationales for its decisions. In the summarization phase, we employ an Integer Linear Programming (ILP) based optimization technique along with the help of rationales to generate summaries of event categories. Extensive evaluation on large-scale disaster events shows (a). our model can classify tweets into disaster-related categories with an 85% Macro F1 score and high interpretability (b). the summarizer achieves (5-25%) improvement in terms of ROUGE-1 F-score over most state-of-the-art approaches.

AB - Microblogging platforms like Twitter have been heavily leveraged to report and exchange information about natural disasters. The real-time data on these sites is highly helpful in gaining situational awareness and planning aid efforts. However, disaster-related messages are immersed in a high volume of irrelevant information. The situational data of disaster events also vary greatly in terms of information types ranging from general situational awareness (caution, infrastructure damage, casualties) to individual needs or not related to the crisis. It thus requires efficient methods to handle data overload and prioritize various types of information. This paper proposes an interpretable classification-summarization framework that first classifies tweets into different disaster-related categories and then summarizes those tweets. Unlike existing work, our classification model can provide explanations or rationales for its decisions. In the summarization phase, we employ an Integer Linear Programming (ILP) based optimization technique along with the help of rationales to generate summaries of event categories. Extensive evaluation on large-scale disaster events shows (a). our model can classify tweets into disaster-related categories with an 85% Macro F1 score and high interpretability (b). the summarizer achieves (5-25%) improvement in terms of ROUGE-1 F-score over most state-of-the-art approaches.

KW - Classification

KW - Crisis Events

KW - Interpretability

KW - Summarization

UR - http://www.scopus.com/inward/record.url?scp=85129805838&partnerID=8YFLogxK

U2 - 10.1145/3485447.3512259

DO - 10.1145/3485447.3512259

M3 - Conference contribution

AN - SCOPUS:85129805838

SP - 3641

EP - 3650

BT - WWW '22

T2 - 31st ACM World Wide Web Conference, WWW 2022

Y2 - 25 April 2022 through 29 April 2022

ER -