TY - JOUR AU - Shah, Darsh J AU - Yu, Lili AU - Lei, Tao AU - Barzilay, Regina PY - 2021/05/18 Y2 - 2024/03/28 TI - Nutri-bullets: Summarizing Health Studies by Composing Segments JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 15 SE - AAAI Technical Track on Speech and Natural Language Processing II DO - 10.1609/aaai.v35i15.17624 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17624 SP - 13780-13788 AB - We introduce 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 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. ER -