Nutri-bullets: Summarizing Health Studies by Composing Segments

No. 15: AAAI-21 Technical Tracks 15

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

AAAI Technical Track on Speech and Natural Language Processing II

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.

10.1609/aaai.v35i15.17624