# ADSumm: Annotated Ground-truth Summary Datasets for Disaster Tweet Summarization

arXiv: https://arxiv.org/abs/2405.06551 · HTML: https://mlsanity.com/paper/2405.06551/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 91 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [CrisisMMD: Multimodal Twitter Datasets from Natural Disasters](https://mlsanity.com/paper/1805.00713/) (2018)
- [Extractive Summarization via ChatGPT for Faithful Summary Generation](https://mlsanity.com/paper/2304.04193/) (2023)
- [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)
- [CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines](https://mlsanity.com/paper/2204.13746/) (2022)
- [Unsupervised Detection of Sub-events in Large Scale Disasters](https://mlsanity.com/paper/1912.13332/) (2019)
- [Utilizing Microblogs for Assisting Post-Disaster Relief Operations via Matching Resource Needs and Availabilities](https://mlsanity.com/paper/2007.09368/) (2020)
- [empathi: An ontology for Emergency Managing and Planning about Hazard Crisis](https://mlsanity.com/paper/1810.12510/) (2018)
- [RweetMiner: Automatic identification and categorization of help requests on twitter during disasters](https://mlsanity.com/paper/2303.02399/) (2023)
- [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)
- [PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT](https://mlsanity.com/paper/2305.11536/) (2023)
- [IKDSumm: Incorporating Key-phrases into BERT for extractive Disaster Tweet Summarization](https://mlsanity.com/paper/2305.11592/) (2023)
- [Word Storms: Multiples of Word Clouds for Visual Comparison of Documents](https://mlsanity.com/paper/1301.0503/) (2013)
- [TONE: A 3-Tiered ONtology for Emotion analysis](https://mlsanity.com/paper/2401.06810/) (2024)

## Abstract (arXiv)

Online social media platforms, such as Twitter, provide valuable information during disaster events. Existing tweet disaster summarization approaches provide a summary of these events to aid government agencies, humanitarian organizations, etc., to ensure effective disaster response. In the literature, there are two types of approaches for disaster summarization, namely, supervised and unsupervised approaches. Although supervised approaches are typically more effective, they necessitate a sizable number of disaster event summaries for testing and training. However, there is a lack of good number of disaster summary datasets for training and evaluation. This motivates us to add more datasets to make supervised learning approaches more efficient. In this paper, we present ADSumm, which adds annotated ground-truth summaries for eight disaster events which consist of both natural and man-made disaster events belonging to seven different countries. Our experimental analysis shows that the newly added datasets improve the performance of the supervised summarization approaches by 8-28% in terms of ROUGE-N F1-score. Moreover, in newly annotated dataset, we have added a category label for each input tweet which helps to ensure good coverage from different categories in summary. Additionally, we have added two other features relevance label and key-phrase, which provide information about the quality of a tweet and explanation about the inclusion of the tweet into summary, respectively. For ground-truth summary creation, we provide the annotation procedure adapted in detail, which has not been described in existing literature. Experimental analysis shows the quality of ground-truth summary is very good with Coverage, Relevance and Diversity.