# IKDSumm: Incorporating Key-phrases into BERT for extractive Disaster Tweet Summarization

arXiv: https://arxiv.org/abs/2305.11592 · HTML: https://mlsanity.com/paper/2305.11592/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 78 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages](https://mlsanity.com/paper/1605.05894/) (2016)
- [HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks](https://mlsanity.com/paper/2104.03090/) (2021)
- [ASPECTNEWS: Aspect-Oriented Summarization of News Documents](https://mlsanity.com/paper/2110.08296/) (2021)
- [Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents](https://mlsanity.com/paper/2106.15876/) (2021)
- [QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications](https://mlsanity.com/paper/2010.14108/) (2020)
- [empathi: An ontology for Emergency Managing and Planning about Hazard Crisis](https://mlsanity.com/paper/1810.12510/) (2018)
- [OntoDSumm : Ontology based Tweet Summarization for Disaster Events](https://mlsanity.com/paper/2201.06545/) (2022)
- [EnDSUM: Entropy and Diversity based Disaster Tweet Summarization](https://mlsanity.com/paper/2203.01188/) (2022)
- [Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers](https://mlsanity.com/paper/2204.10230/) (2022)
- [Summarizing Situational and Topical Information During Crises](https://mlsanity.com/paper/1610.01561/) (2016)

## Built on by (most cited first, 3 of 3)

- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [ATSumm: Auxiliary information enhanced approach for abstractive disaster Tweet Summarization with sparse training data](https://mlsanity.com/paper/2405.06541/) (2024)
- [ADSumm: Annotated Ground-truth Summary Datasets for Disaster Tweet Summarization](https://mlsanity.com/paper/2405.06551/) (2024)

## Abstract (arXiv)

Online social media platforms, such as Twitter, are one of the most valuable sources of information during disaster events. Therefore, humanitarian organizations, government agencies, and volunteers rely on a summary of this information, i.e., tweets, for effective disaster management. Although there are several existing supervised and unsupervised approaches for automated tweet summary approaches, these approaches either require extensive labeled information or do not incorporate specific domain knowledge of disasters. Additionally, the most recent approaches to disaster summarization have proposed BERT-based models to enhance the summary quality. However, for further improved performance, we introduce the utilization of domain-specific knowledge without any human efforts to understand the importance (salience) of a tweet which further aids in summary creation and improves summary quality. In this paper, we propose a disaster-specific tweet summarization framework, IKDSumm, which initially identifies the crucial and important information from each tweet related to a disaster through key-phrases of that tweet. We identify these key-phrases by utilizing the domain knowledge (using existing ontology) of disasters without any human intervention. Further, we utilize these key-phrases to automatically generate a summary of the tweets. Therefore, given tweets related to a disaster, IKDSumm ensures fulfillment of the summarization key objectives, such as information coverage, relevance, and diversity in summary without any human intervention. We evaluate the performance of IKDSumm with 8 state-of-the-art techniques on 12 disaster datasets. The evaluation results show that IKDSumm outperforms existing techniques by approximately 2-79% in terms of ROUGE-N F1-score.