{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/led-down-the-rabbit-hole-exploring-the","title":"LED down the rabbit hole: exploring the potential of global attention for biomedical multi-document summarisation","arxiv_id":"2209.08698","date":"2022-09-19","proceeding":"sdp (COLING) 2022 10","authors":["Yulia Otmakhova","Hung Thinh Truong","Timothy Baldwin","Trevor Cohn","Karin Verspoor","Jey Han Lau"],"abstract":"In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task. Specifically, we adapt PRIMERA (Xiao et al., 2022) to the biomedical domain by placing global attention on important biomedical entities in several ways. We analyse the outputs of the 23 resulting models, and report patterns in the results related to the presence of additional global attention, number of training steps, and the input configuration.","url_abs":"https://arxiv.org/abs/2209.08698v1","url_pdf":"https://arxiv.org/pdf/2209.08698v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"led-down-the-rabbit-hole-exploring-the","repo_url":"https://github.com/allenai/mslr-shared-task","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"led-down-the-rabbit-hole-exploring-the","repo_url":"https://github.com/joey234/primer-pico-attn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}