Toward Structured Related Work Generation with Novelty Statements (2024)

Abstract: To help readers understand the novelty and the research context, an excellent related work section is structured ( i.e., the section consists of paragraphs determined by categorizing papers into several topics) and includes descriptions of novelty. However, previous studies viewed related work generation as multi-document summarization, and the structure and novelty statement are ignored in such studies. In this paper, we redefine the related work generation task as summarization with structure ( i.e., multiple paragraphs with citation) and novelty statement. For this task, we propose a quality-oriented dataset and evaluation metrics. Experiments evaluated the state-of-the-art language models on our tasks, and we confirmed the issues with the current models and the validity of the evaluation indicators.

## introduction a related work section of a scientific article plays an important role in helping readers understand the context of the research field and the novelty of their work @xcite . the well-written related work is properly structured (i.e., make paragraphs with topics determined from the relations between current work and previous works) and clearly described novelty. to write such a related work section, authors must categorize a number of articles into several topics, summarize them, and highlight the novelty compared to them, which requires a deep and broad understanding of the research field, and is, of course, a very time-consuming process. given the difficulty, the automatic related work generation has been proposed @xcite . however, existing studies viewed the related work generation as multi-input # target article information 𝑡 title: scott: self-consistent chain-of-thought distillation abstract: existing neural models have difficulty generalizing.… # a set of cited articles [1] title:{title}, abstract: {abstract} [2] title: {title}, abstract: {abstract} : [n] title: {title}, abstract: {abstract} ## related work extractive related work generation. the prototype of automated related work section generation has been proposed by @xcite . they viewed the task as a topic-biased summarization and proposed an extraction method by selecting sentences based on given topics. inheriting this setting, several extractive summarization methods have been proposed by @xcite ; @xcite ; @xcite ; @xcite . abstractive related work generation. the first trial of abstractive related work generation is citation sentence generation @xcite , and afterward, a paragraph of related work has also attracted attention @xcite @xcite . for example, @xcite has proposed target-aware related work generation to generate target-centric related work. @xcite has improved quality and readability with the causality invention module. there are discussions about the task of the related work generation. @xcite has proposed a task that combines reference retrieval and related work generation. @xcite unified previous datasets and defined related work generation as cited generation task. however, these studies viewed related work generation as a summarization task and did not focus on the structure and novelty of related work. unlike previous studies, we aim to generate structured related work with novelty statements. ## conclusion in this paper, we redefined automatic related work generation as structured related work generation with novelty statement (strogens). for strogens, we proposed datasets focusing on the trade-off of quality and quantity and automatic evaluation metrics for structure and novelty. using the quantity-oriented and quality-oriented datasets, we evaluated the performance of state-of-the-art models and showed that the task is not solved by simple summarization ability, and the ability to handle input length is required. in addition, we conducted human evaluation and demonstrated the effectiveness of the proposed automated evaluation metrics.