IITP - AI - NLP - ML @ CL - S ci S umm 2020, CL - L ay S umm 2020, L ong S umm 2020 (2020)

Abstract: The publication rate of scientific literature increases rapidly, which poses a challenge for researchers to keep themselves updated with new state-of-the-art. Scientific document summarization solves this problem by summarizing the essential fact and findings of the document. In the current paper, we present the participation of IITP-AI-NLP-ML team in three shared tasks, namely, CL-SciSumm 2020, LaySumm 2020, LongSumm 2020, which aims to generate medium, lay, and long summaries of the scientific articles, respectively. To solve CL-SciSumm 2020 and LongSumm 2020 tasks, three well-known clustering techniques are used, and then various sentence scoring functions, including textual entailment, are used to extract the sentences from each cluster for a summary generation. For LaySumm 2020, an encoder-decoder based deep learning model has been utilized. Performances of our developed systems are evaluated in terms of ROUGE measures on the associated datasets with the shared task.

## introduction massive amounts of scientific articles are published day by day @xcite @xcite , which impose a big challenge for researchers in various fields to keep themselves upto-date with the new developments. a bibliometric analyst's study shows that after nine years, the number of published articles will be doubled @xcite . the scientific document summarization objective is to provide a summary of the reference paper. this summary should contain all the important facts. therefore, it reduces the human effort to understand the document. challenges of each style of the summary are as follows: the current paper addresses this issue by participating in the three shared tasks, namely, cl-scisumm 2020, laysumm 2020, longsumm 2020 @xcite . these tasks' goals are to generate medium, lay (understandable for the non-technical audience), and long summaries of the scientific articles. we are using an extractive approach for cl-scisumm, for longsumm, an extractive followed by an abstractive approach is utilized, and for laysumm, an abstractive approach is utilized. the detailed descriptions of these tasks are provided in the subsequent sections. ## cl-scisumm 2020 it is the sixth shared task on scientific document summarization. in the literature, two approaches have been used to solve this problem. the first one considers abstract as the summary of the paper, but the problem with the approach is it provides only the theme of the paper. the abstract may not convey all the important points of the summary @xcite . therefore, the second approach has been followed to solve the scientific summarization, which is citation-based summarization @xcite . it utilizes a set of citations referencing to the original article (reference paper to be summarized). citations are short descriptions that explain the reference paper all its contributions; this text can be termed as citation text or citance. ## cl-laysumm 2020 the motivation of the cl-laysumm shared task is to automatically produce lay summaries of technical (scientific research article) texts. a lay summary is defined as a textual summary easily understood by a non-technical audience. it is typically produced either by the authors or by a journalist or commentator. the corpus released in shared tasks covers three distinct domains: epilepsy, archeology, and materials engineering. in a lay summary, there should not be any technical jargon. it should reflect the overall scope, goal, and potential impact of a scientific paper. it is typically less than 150 words in length. the objective is to generate summaries that represent the content, understandable and interesting to a lay audience. ## longsumm 2020 in all the previous works of scientific summarization @xcite , there is a summary length constraint of a maximum of 250 words. but in the current longsumm shared task, the generated summary can be the length of between 100-1500 words. ## conclusion this paper has presented the results of participation of the iitp-ai-nlp-ml team in three shared tasks, namely, cl-scisumm 2020, cl-laysumm 2020, longsumm 2020, at sdp 2020. for cl-scisumm, three sub-tasks are there: task 1(a), task 1(b), and task 2. for task 1 (a), we have utilized wmd to extract the cited text span from the reference paper; for task 1 (b), the similarity-based measure has been used to identify the facet of each cited text span. task 2 is based on clustering, followed by sentence extraction from each cluster based on their relevance/score. for longsumm, we have utilized clustering and deep learning techniques and reported 13 different ways to generate a long summary. for laysumm, we have proposed a deep learning-based encoder-decoder model that generates the lay summary utilizing the fine-tuned bert language model's embedding.