{"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/nutri-bullets-summarizing-health-studies-by","title":"Nutri-bullets: Summarizing Health Studies by Composing Segments","arxiv_id":"2103.11921","date":"2021-03-22","proceeding":null,"authors":["Darsh J Shah","Lili Yu","Tao Lei","Regina Barzilay"],"abstract":"We introduce \\emph{Nutri-bullets}, a multi-document summarization task for health and nutrition. First, we present two datasets of food and health summaries from multiple scientific studies. Furthermore, we propose a novel \\emph{extract-compose} model to solve the problem in the regime of limited parallel data. We explicitly select key spans from several abstracts using a policy network, followed by composing the selected spans to present a summary via a task specific language model. Compared to state-of-the-art methods, our approach leads to more faithful, relevant and diverse summarization -- properties imperative to this application. For instance, on the BreastCancer dataset our approach gets a more than 50\\% improvement on relevance and faithfulness.\\footnote{Our code and data is available at \\url{https://github.com/darsh10/Nutribullets.}}","url_abs":"https://arxiv.org/abs/2103.11921v1","url_pdf":"https://arxiv.org/pdf/2103.11921v1.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":"nutri-bullets-summarizing-health-studies-by","repo_url":"https://github.com/darsh10/Nutribullets","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"multi-document-summarization","task_name":"Multi-Document Summarization"},{"task_slug":"nutrition","task_name":"Nutrition"}],"methods":[],"datasets_introduced":[{"slug":"healthline","name":"Healthline","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.11921","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}