Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using TransformersRelevant and timely information collected from social media during crises can be an invaluable resource for emergency management. However, extracting this information remains a challenging task, particularly when dealing with social media postings in multiple languages. This work proposes a cross-lingual method for retrieving and summarizing crisis-relevant information from social media postings. We describe a uniform way of expressing various information needs through structured queries and a way of creating summaries answering those information needs. The method is based on multilingual transformers embeddings. Queries are written in one of the languages supported by the embeddings, and th
- doi
- 10.1145/3511095.3531279
- name
- Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers
- source
- acm-html-via-r.jina.ai
- acm_url
- https://dl.acm.org/doi/10.1145/3511095.3531279
- authors
- Fedor Vitiugin, Carlos Castillo
- doi_url
- https://doi.org/10.1145/3511095.3531279
- license
- © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
- summary
- Relevant and timely information collected from social media during crises can be an invaluable resource for emergency management. However, extracting this information remains a challenging task, particularly when dealing with social media postings in multiple languages. This work proposes a cross-lingual method for retrieving and summarizing crisis-relevant information from social media postings. We describe a uniform way of expressing various information needs through structured queries and a way of creating summaries answering those information needs. The method is based on multilingual transformers embeddings. Queries are written in one of the languages supported by the embeddings, and th
- keywords
- abstractive summarization, multilingual retrieval, social media, emergency management
- source_pdf
- HT-2022_39_31_3511095/3511095.3531279.pdf
- import_kind
- full_text
- displayAuthor
- Fedor Vitiugin, Carlos Castillo
- displayPublishTime
- 2022-06-28
- source_attribution
- Formatting converted from the ACM version of record under supplied ACM publication authorization.
Powered by Seed HypermediaOpen App