Details
| Original language | English |
|---|---|
| Title of host publication | WWW '22 |
| Subtitle of host publication | Proceedings of the ACM Web Conference 2022 |
| Pages | 3641-3650 |
| Number of pages | 10 |
| ISBN (electronic) | 9781450390965 |
| Publication status | Published - 25 Apr 2022 |
| Event | 31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France Duration: 25 Apr 2022 → 29 Apr 2022 |
Abstract
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.
Keywords
- Classification, Crisis Events, Interpretability, Summarization
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
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WWW '22: Proceedings of the ACM Web Conference 2022. 2022. p. 3641-3650.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
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 -