GUIR @ M u P 2022: Towards Generating Topic-aware Multi-perspective Summaries for Scientific Documents (2022)

Abstract: This paper presents our approach for the MuP 2022 shared task —-Multi-Perspective Scientific Document Summarization, where the objective is to enable summarization models to explore methods for generating multi-perspective summaries for scientific papers. We explore two orthogonal ways to cope with this task. The first approach involves incorporating a neural topic model (i.e., NTM) into the state-of-the-art abstractive summarizer (LED); the second approach involves adding a two-step summarizer that extracts the salient sentences from the document and then writes abstractive summaries from those sentences. Our latter model outperformed our other submissions on the official test set. Specifically, among 10 participants (including organizers’ baseline) who made their results public with 163 total runs. Our best system ranks first in Rouge-1 (F), and second in Rouge-1 (R), Rouge-2 (F) and Average Rouge (F) scores.

## introduction scientific text summarization has received growing interest over the recent years @xcite @xcite @xcite @xcite , although it has been studied from years before @xcite @xcite @xcite . generating scientific summaries is deemed to be a challenging task, given the specific characteristics of scientific documents such as extreme document length, presence of complex domain-specific concepts, and specific structure, where the information is framed within sections. these characteristics of scientific papers, coupled with the aim of generating shorter or longer form summaries, call for special model considerations to deal with the challenging task of summarization. researchers have looked into various approaches of unsupervised, supervised, neural, utilizing citations, knowledge, context, etc in generating the summaries in an extractive or abstractive way. the existing evaluation systems in scientific summarization assume one signle gold summary for each scientific paper, based on which the summary generator optimizes the generation. the motivation of the mup shared task @xcite is to provide multiple gold summaries per document so that the generated systems can be evaluated based on how well they captured various aspects of the paper into their summary. the assumption is that a single gold summary may not include multiple aspects expressed in the paper, as the writing of a summary is subjective. specifically, the mup organizers introduce a novel english summarization dataset collected from scientific peer reviews to reflect multiple perspectives from reviewers' standpoints. the participating teams are then asked to produce a scientific summary that can express diverse viewpoints on a given document. in this study, we extend the longformer encoder-decoder (led) abstractive summarization model @xcite . in our experiments, we specifically explore two distinct approaches: (1) incorporating a neural topic modeling approach @xcite to the led summarizer; and (2) proposing a two-step led-based summarizer that first extracts the salient sentences and then performs abstraction over the extracted sentences to produce a multi-perspective summary. our intuition of these extensions is that each perspective of a paper may focus on specific sets of topics which are discussed within specific sets of sentences, that should be taken into account by the summarizer. to benefit from the advantages of each of these approaches, we further combine them and propose a topic-aware two-step summarizer. our combined model achieves the best results amongst the other settings on the validation and led (encoder) sentence scorer 0.4 <@xmath0.2 0.1 0.9 0.1 led (decoder) <@xmath1. specifically, it attains the first rank in rouge-1 (f), and second in rouge-1 (r), rouge-2 (f), and average rouge (f) scores, with 1.4% relative improvement over the baseline in terms of average rouge (f) scores. ## model the general overview of our model is demonstrated in figure 1 . our summarizer is composed of multiple components, including an led encoder, a neural topic modeling layer, an information gating layer, and an extractor layer, followed by an led decoder. in what follows, we explain the details of our proposed model. ## related work while scientific document summarization has a long history, it has recently gained increasing attention from research communities. previous works have approached this problem by either generating regular-length summaries, such as @xcite among many, or very recently so-called extended summaries @xcite @xcite . these attempts include hierarchical sequence modeling @xcite @xcite , citation-context based approaches @xcite @xcite , using documents' structural information as saliency signals @xcite @xcite , two-phase summarization models @xcite . up to recently, majority of existing work in scientific domain has evaluated the systems assuming that there is only one gold summary per paper. mup challenge is the first attempt toward evaluation of summarization systems given multiple gold summaries, each of which captures a specific aspect of the paper. ## conclusion in this study, we explore two summarization approaches to tackle the multi-perspective summary generation task, organized by the mup challenge. our first model learns a latent topic distribution using neural topic modeling (ntm) in the fine-tuning stage, and the knowledge is shared between the topic modeling and text summarization task for summary generation. next, as our second model, we further incorporate a two-step summarization framework into the summarization model for yielding even more improvements. our best submission ranks first in rouge-1 (f); and second in rouge-1 (r), rouge-2 (f), and average rouge (f) scores.