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The Scholarly Document Processing (SDP) workshop is to encourage more effortson natural language understanding of scientific task. It contains three sharedtasks and we participate in the LongSumm shared task. In this paper, wedescribe our text summarization system, SciSummPip, inspired by SummPip (Zhaoet al., 2020) that is an unsupervised text summarization system formulti-document in news domain. Our SciSummPip includes a transformer-basedlanguage model SciBERT (Beltagy et al., 2019) for contextual sentencerepresentation, content selection with PageRank (Page et al., 1999), sentencegraph construction with both deep and linguistic information, sentence graphclustering and within-graph summary generation. Our work differs from previousmethod in that content selection and a summary length constraint is applied toadapt to the scientific domain. The experiment results on both training datasetand blind test dataset show the effectiveness of our method, and we empiricallyverify the robustness of modules used in SciSummPip with BERTScore (Zhang etal., 2019a).

SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline