We present SciA11y, a system that renders inaccessible scientific paper PDFs into HTML. SciA11y uses machine learning models to extract and understand the content of scientific PDFs, and reorganizes the resulting paper components into a form that better supports skimming and scanning for blind and low vision (BLV) readers. SciA11y adds navigation features such as tagged headings, a table of contents, and bidirectional links between inline citations and references, which allow readers to resolve citations without losing their context. A set of 1.5 million open access papers are processed and available at https://scia11y.org/. This system is a first step in addressing scientific PDF accessibility, and may significantly improve the experience of paper reading for BLV users.

To assess the effectiveness of any medical intervention, researchers must conduct a timeintensive and manual literature review.NLP systems can help to automate or assist in parts of this expensive process.In support of this goal, we release MSˆ2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20K summaries derived from the scientific literature.This dataset facilitates the development of systems that can assess and aggregate contradictory evidence across multiple studies, and is the first large-scale, publicly available multi-document summarization dataset in the biomedical domain.We experiment with a summarization system based on BART, with promising early results, though significant work remains to achieve higher summarization quality.We formulate our summarization inputs and targets in both free text and structured forms and modify a recently proposed metric to assess the quality of our system's generated summaries.

Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph.It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections.In this paper, we introduce SCIREX, a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level N -ary relation identification from scientific articles.We annotate our dataset by integrating automatic and human annotations, leveraging existing scientific knowledge resources.We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to documentlevel IE.Analyzing the model performance shows a significant gap between human performance and current baselines, inviting the community to use our dataset as a challenge to develop document-level IE models.

David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.

Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.