Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL - S ci S umm, L ay S umm and L ong S umm (2020)

Abstract: We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm. We report on each of the tasks, which received 18 submissions in total, with some submissions addressing two or three of the tasks. In summary, the quality and quantity of the submissions show that there is ample interest in scholarly document summarization, and the state of the art in this domain is at a midway point between being an impossible task and one that is fully resolved.

## introduction scientific documents constitute a rich field for different tasks such as reference string parsing, citation intent classification, summarization and more. the constantly increasing number of scientific publications raises additional issues such as making these publications accessible to non-expert readers, or, on the other hand, to experts who are interested in a deeper understanding of the paper without reading a paper in full. for this year's scholarly document processing workshop @xcite at emnlp 2020, we proposed three tasks: cl-scisumm, lay-summ and longsumm to improve the state of the art for different aspects of scientific document summarization. the cl-scisumm task was introduced in 2014 and aims to explore the summarization of scientific research in the domain of computational linguistics research. it encourages the incorporation of new kinds of information in automatic scientific paper summarization, such as the facets of research information being summarized in the research paper. cl-scisumm also encourages the use of citing mini-summaries written in other papers, by other scholars, when they refer to the paper. laysumm (lay summarization) addresses the issue of making research results available to a larger audience by automatically generating 'lay summaries', or summaries that explain the science contained within the paper in laymen's terms. finally, the longsumm (long scientific document summarization) task focuses on generating long summaries of scientific text. it is fundamentally different than generating short summaries that mostly aim at teasing the reader. the longsumm task strives to learn how to cover the salient information conveyed in a given scientific document, taking into account the characteristics and the structure of the text. the motivation for longsumm was first demonstrated by the ibm science summarizer system, @xcite that retrieves and creates long summaries of scientific documents foot_0 . while @xcite studied some use-cases and proposed a summarization approach with some human evaluation, the authors stressed the need of a large dataset that will unleash the research in this domain. longsumm aims at filling this gap by providing large dataset of long summaries which are based on blogs written by machine learning and nlp experts. in this paper we present the tasks, datasets, description of the participating systems, and provide their results and insights from shared tasks. ## conclusion the first scholarly document processing workshop @xcite comprise three summarization tasks, that each aimed to improve the state-of-the-art of scientific document summarization. in total, we received 18 submissions that addressed one or more of these tasks. it was a