CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating Long Summaries

Abstract

The huge influx of published papers in the field of machine learning makes the task of summarization of scholarly documents vital, not just to eliminate the redundancy but also to provide a complete and satisfying crux of the content. We participated in LongSumm 2021: The 2 nd Shared Task on Generating Long Summaries for scientific documents, where the task is to generate long summaries for scientific papers provided by the organizers. This paper discusses our extractive summarization approach to solve the task. We used TextRank algorithm with the BM25 score as a similarity function. Even after being a graph-based ranking algorithm that does not require any learning, Tex-tRank produced pretty decent results with minimal compute power and time. We attained 3 rd rank according to ROUGE-1 scores (0.5131 for F-measure and 0.5271 for recall) and performed decently as shown by the ROUGE-2 scores.

TSTR: Too Short to Represent, Summarize with Details! Intro-Guided Extended Summary Generation

“…The proposed models for the extended summary generation task include jointly learning to predict sentence importance and sentence section to extract top sentences (Sotudeh et al, 2020); utilizing section-contribution computations to pick sentences from important section for forming the final summary (Ghosh Roy et al, 2020); identifying salient sections for generating abstractive summaries (Gidiotis et al, 2020); ensembling of extraction and abstraction models to form final summary (Ying et al, 2021); an extractive model with TextRank algorithm equipped with BM25 as similarity function (Kaushik et al, 2021); and incorporating sentences embeddings into graph-based extractive summarizer in an unsupervised manner (Ramirez-Orta and Milios, 2021). Unlike these works, we do not exploit any sectional nor citation information in this work.…”