IIITBH - IITP @ CL - S ci S umm20, CL - L ay S umm20, L ong S umm20 (2020)

Abstract: In this paper, we present the IIIT Bhagalpur and IIT Patna team’s effort to solve the three shared tasks namely, CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020 at SDP 2020. The theme of these tasks is to generate medium-scale, lay and long summaries, respectively, for scientific articles. For the first two tasks, unsupervised systems are developed, while for the third one, we develop a supervised system. The performances of all the systems were evaluated on the associated datasets with the shared tasks in term of well-known ROUGE metric.

## introduction due to a lot of research going into the computational linguistic (cl) domain as well as in other domains, the rate of publishing scientific articles has been increased and will continue to expand @xcite @xcite . this makes the researchers challenging to update them with the up-to-date advancements. a survey (review) article may help the researcher to have a gist of the recent advancements. but, writing a survey paper is a very laborious and time-consuming task. this challenge demands summarization of scientific articles @xcite ) by providing their summary in a few words and then prepare the survey article . but sometimes, for niche practitioners, the published and survey articles may be difficult to understand. to make them relevant for the nonpractitioners and to benefit all the researchers, it is indeed a need to outline the contribution of research articles in lay language. the current paper demonstrates the participation of iiit bhagapur and iit patna team in three shared tasks namely, cl-scisumm 2020, longsumm 2020 and cl-laysumm 2020, at first workshop on schol-ary document processing foot_0 , 2020 @xcite . the theme of these tasks is to generate medium-scale, long and lay summaries, respectively. here, lay summary means a textual summary which is intended for non-technical audience. the scientific articles used for the first and third tasks are related to computational linguistic domain. while, for the second task, scientific articles cover distinct domains: archeology, epilepsy, and materials engineering. in current paper, all these tasks are posed as extractive summarization @xcite problems where a subset of sentences are selected from scientific articles based on their relevance. for cl-laysumm and cl-scisumm, we have developed the system based on the maximal marginal relevance (mmr) @xcite which considers novelty and informativeness of sentences with respect to what is already included in the summary. and, for longsumm, our system utilizes neural network based approach. more descriptions about these tasks including datasets and methodology used, are provided in the subsequent sections. the performances of the systems are evaluated in terms of rouge (1-gram, 2-gram, and l) metrics on the provided dataset. ## cl-scisumm 2020 cl-scisumm 2020 is the sixth computational linguistics scientific document summarization shared task which aims to generate summaries of scientific articles not exceeding 250 words. the associated dataset for the task is provided with a reference paper (rp) (the paper to be summarized) and 10 or more citing papers (cps) containing citations to the rp, which are used to summarise rp. it includes two more sub-tasks: (a) task 1(a)iden-tifying the text-spans in the reference article that mostly reflect the citation contexts (i.e., citances that cite the rp) of the citing articles; (b) task 1(b)categorizing the identified text-spans into a predefined set of facets. generation of structured summary for scientific document summmarization using the identified text-spans is covered in task 2. ## cl-laysumm 2020 the cl-laysumm 2020, which is the first shared task 3 for lay summary generation, is for automatic generation of lay summary in 70-100 words which is readable and easily understandable by the general pubic. in other words, given a full-text paper and its abstract, the task is to generate a lay summary of the specified length of that paper. ## longsumm 2020 most of the existing works on scientific document summarization focus on generating a summary of shorter length (maximum up to 250 words). such type of length constraint can be sufficient when summarizing news articles, but for scientific articles, the summary requires expertise in the scientific domain to understand it. longsumm 2020 shared task addresses this issue by generating longer summaries (up to 600 words) of scientific articles. ## conclusion and future work we have investigated the effects of using maximal marginal relevance (mmr) in developing the systems for three shared tasks: cl-scisumm, cl-laysumm, and longsumm 2020. another variant of mmr is also proposed by incorporating a length-based feature. for longsumm, we have also investigated the effect of using a convolution neural network. as the goal of laysumm is to generate lay summary, which is understandable for a non-technical audience, we have tried a common word removal approach using the lexical database like wordnet, which fails due to non-presence of scientific terms. in the future, we would like to develop a more sophisticated approach for laysumm generation.