We study the zero-shot setting for the aspect-based scientific document summarization task. Summarizing scientific documents with respect to an aspect can remarkably improve document assistance systems and readers experience. However, existing large-scale datasets contain a limited variety of aspects, causing summarization models to over-fit to a small set of aspects and a specific domain. We establish baseline results in zero-shot performance (over unseen aspects and the presence of domain shift), paraphrasing, leave-one-out, and limited supervised samples experimental setups. We propose a self-supervised pre-training approach to enhance the zero-shot performance. We leverage the PubMed structured abstracts to create a biomedical aspect-based summarization dataset. Experimental results on the PubMed and FacetSum aspect-based datasets show promising performance when the model is pre-trained using unlabelled in-domain data.

In this paper we present a prototype demonstrator showcasing a novel method to perform semantic exploration of user reviews. The system enables effective navigation in a rich contextual semantic schema with a large number of structured classes indicating relevant information. In order to identify instances of the structured classes in the reviews, we defined a new Information Extraction task called Semantic Context Path (SCP) labeling, which simultaneously assigns types and semantic roles to entity mentions. Reviews can rapidly be explored based on the fine-grained and structured semantic classes. As a proof-of-concept, we have implemented this system for reviews on Points-of-Interest, in English and Korean.