WikiHow: A Large Scale Text Summarization Dataset
Mahnaz Koupaee University of California, Santa Barbara、William Yang Wang University of California, Santa Barbara
Abstract
Sequence-to-sequence models have recently gained the state of the art performance in summarization. However, not too many large-scale high-quality datasets are available and almost all the available ones are mainly news articles with specific writing style. Moreover, abstractive human-style systems involving description of the content at a deeper level require data with higher levels of abstraction. In this paper, we present WikiHow, a dataset of more than 230,000 article and summary pairs extracted and constructed from an online knowledge base written by different human authors. The articles span a wide range of topics and therefore represent high diversity styles. We evaluate the performance of the existing methods on WikiHow to present its challenges and set some baselines to further improve it.
中文速览
现有的文本摘要数据集几乎清一色是新闻文章，篇幅有限、写作风格单一，且抽象程度不足，难以推动更高水平摘要系统的发展。为此，研究者从WikiHow在线知识库中抓取并构建了一个名为WikiHow的大规模数据集，涵盖20个类别、超过20万篇文章，通过将每个步骤的粗体概括句拼接成摘要、将详细说明拼接成原文的方式自动生成文章-摘要对。实验表明，现有抽取式和生成式摘要系统在WikiHow上的ROUGE得分比在CNN/Daily Mail上低约10分，且"前3句"基线表现极差，说明WikiHow的非倒金字塔写作结构和更高抽象压缩比给模型带来了显著挑战。这一数据集的发布为摘要研究提供了风格多样、抽象度更高的基准，有助于推动模型突破对单一新闻语料的依赖。
原文 arXiv:1810.09305;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1810.09305v1