Automatic literature review generation is one of the most challenging tasks
in natural language processing. Although large language models have tackled
literature review generation, the absence of large-scale datasets has been a
stumbling block to the progress. We release SciReviewGen, consisting of over
10,000 literature reviews and 690,000 papers cited in the reviews. Based on the
dataset, we evaluate recent transformer-based summarization models on the
literature review generation task, including Fusion-in-Decoder extended for
literature review generation. Human evaluation results show that some
machine-generated summaries are comparable to human-written reviews, while
revealing the challenges of automatic literature review generation such as
hallucinations and a lack of detailed information. Our dataset and code are
available at this https URL

通过 SciReviewGen 数据集，我们评估了最近基于 Transformer 的文献综述生成模型在文献综述生成任务上的表现，其中包含用于文献综述生成扩展的 Fusion-in-Decoder 模型，并且发现自动生成的摘要已经可以和人工撰写的综述媲美，但是也揭示出文献综述自动生成的挑战，例如幻觉和缺乏详细信息。

SciReviewGen：用于自动文献综述生成的大规模数据集

SciReviewGen: A Large-scale Dataset for Automatic Literature Review  Generation

Recent Transformer-based summarization models have provided a promising
approach to abstractive summarization. They go beyond sentence selection and
extractive strategies to deal with more complicated tasks such as novel word
generation and sentence paraphrasing. Nonetheless, these models have two
shortcomings: (1) they often perform poorly in content selection, and (2) their
training strategy is not quite efficient, which restricts model performance. In
this paper, we explore two orthogonal ways to compensate for these pitfalls.
First, we augment the Transformer network with a sentence cross-attention
module in the decoder, encouraging more abstraction of salient content. Second,
we include a curriculum learning approach to reweight the training samples,
bringing about an efficient learning procedure. Our second approach to enhance
the training strategy of Transformers networks makes stronger gains as compared
to the first approach. We apply our model on extreme summarization dataset of
Reddit TIFU posts. We further look into three cross-domain summarization
datasets (Webis-TLDR-17, CNN/DM, and XSum), measuring the efficacy of
curriculum learning when applied in summarization. Moreover, a human evaluation
is conducted to show the efficacy of the proposed method in terms of
qualitative criteria, namely, fluency, informativeness, and overall quality.

本文提出两个方法以弥补 Transformer-based summarization 模型在内容选择方面表现不佳和训练策略效率不高的缺陷，使得模型可以更好的理解要点，同时引入课程学习的方法提高了训练速度和质量，并用 Reddit TIFU 数据集和其他三个跨领域总结测量我们的模型的有效性，同时进行了人类评估表明所提出的方法在流畅性、信息量和整体质量等质量标准上具有良好的效果。