Team AINLPML @ M u P in SDP 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach (2022)

Abstract: This paper introduces the proposed summarization system of the AINLPML team for the First Shared Task on Multi-Perspective Scientific Document Summarization at SDP 2022. We present a method to produce abstractive summaries of scientific documents. First, we perform an extractive summarization step to identify the essential part of the paper. The extraction step includes utilizing a contributing sentence identification model to determine the contributing sentences in selected sections and portions of the text. In the next step, the extracted relevant information is used to condition the transformer language model to generate an abstractive summary. In particular, we fine-tuned the pre-trained BART model on the extracted summary from the previous step. Our proposed model successfully outperformed the baseline provided by the organizers by a significant margin. Our approach achieves the best average Rouge F1 Score, Rouge-2 F1 Score, and Rouge-L F1 Score among all submissions.

## introduction automatic summarization involves distilling a document down to its essentials. there are two types of summarization techniques: abstractive summarization and extractive summarization. abstractive summarization examines a document and creates a summary from it that may contain phrases that do not present in the original text. the more challenging goal is abstractive summarization, which is beneficial in fields like novels where phrases taken out of context are not a good foundation for producing a grammatical and cohesive summary. we are interested in summarizing scientific literature in this instance. summarization of research papers can help in obtaining core ideas instantly and would help researchers all around the world in fastening the process of literature surveys. it is well recognized that creating summaries of scientific papers is a difficult endeavour. the main question is why the article's abstract doesn't suffice since it summarizes the scientific article. although an abstract has been written, there are many reasons for generating article summaries. first, one of the main problems with abstracts is that they do not include relevant information from the full text. second, it presents the author's viewpoint on the unique characteristic in an incomplete and biased manner @xcite . thirdly, no single summary meets all the user's needs @xcite . in addition, the abstract does not cover all the impacts and contributions of the article @xcite but rather what the author wishes to emphasize. as a result, the summary generated by such a system should be informative enough, cover all the critical sections of the input article, and provide the reader with essential information. furthermore, @xcite discuss the impact factor of a scientific article. summarization systems should accommodate the viewpoints of other researchers (i.e., citations) and the significant aspects highlighted by the article's authors in the abstract since the significance of papers may change over time. most existing summarizing research assumes only one best gold summary for each given material. having just one gold summary limits our capacity to assess the effectiveness of summarizing algorithms because creating summaries is important to derive the significant aspects of any long document. furthermore, because it takes subject matter experts a lot of time to read and comprehend lengthy scientific publications, annotating several gold summaries for scientific documents can be very expensive. the workshops aimed to promote the exploration of strategies for producing multi-perspective summaries. a novel summarizing corpus was provided that used information from peer-reviewed scientific articles to capture various viewpoints from the reader's perspective. in many different branches of science, peer reviews typi-cally begin with a paragraph that summarizes the most important contributions made by a work from the perspective of the reviewer, and each paper typically undergoes a number of different reviews. this paper presents our approach to the mup shared task @xcite . we present an endto-end approach to generate summaries of long scientific documents that uses the advantages of both extractive and abstractive approaches. before producing a summary in an abstractive manner, we perform the extractive step, which is then used for conditioning the abstractor module. we first determined the section of a research paper. we took the abstract, and the last few sentences of the introduction section as mostly authors summarize a few critical questions about the paper in these, such as, 'what is the contribution in the paper?', 'what is the novelty?', 'how is it different from previous works?'. from the rest of the portion of the document, we extracted the contributing sentences using a large language model named contrisci @xcite . contrisci is a bert fine-tuned over sectional data from a research paper, capable of generating binary labels for a given sentence in that section which tells us if the sentence is contributing to the understanding of the section or not. after performing these extractive steps, we trained an abstractive model to form a final summary. our experiments showed that jointly using extractive and abstractive models improves the summarization results. ## methodology we propose an end-to-end pipeline approach to generate summaries automatically from scientific documents. figure 1 shows an overview of our approach. we describe each component briefly as follows: ## experiments in this section we discuss our results and analysis. the data set description (a) and experimental settings (b) can be found in the appendix section. ## conclusions in this paper, we studied the multi-perspective scientific document summarization task. we experimented with a joint model using extractive and abstractive approaches. the extractive approach supports the modelling of the document structure with a strong focus on which parts/sentences of a research paper to attend to while composing a summary, which significantly boosts the quality of the resultant output. on blind test corpora, our system ranks first wrt. to average rouge f1 score. the results motivate towards experimenting with better extractive approaches in future which can improve the generation of abstractive summaries by feeding them ideal input data.