  The Scholarly Document Processing (SDP) workshop is to encourage more efforts
on natural language understanding of scientific task. It contains three shared
tasks and we participate in the LongSumm shared task. In this paper, we
describe our text summarization system, SciSummPip, inspired by SummPip (Zhao
et al., 2020) that is an unsupervised text summarization system for
multi-document in news domain. Our SciSummPip includes a transformer-based
language model SciBERT (Beltagy et al., 2019) for contextual sentence
representation, content selection with PageRank (Page et al., 1999), sentence
graph construction with both deep and linguistic information, sentence graph
clustering and within-graph summary generation. Our work differs from previous
method in that content selection and a summary length constraint is applied to
adapt to the scientific domain. The experiment results on both training dataset
and blind test dataset show the effectiveness of our method, and we empirically
verify the robustness of modules used in SciSummPip with BERTScore (Zhang et
al., 2019a).
