It is very common to use quotations (quotes) to make our writings more
elegant or convincing. To help people find appropriate quotes efficiently, the
task of quote recommendation is presented, aiming to recommend quotes that fit
the current context of writing. There have been various quote recommendation
approaches, but they are evaluated on different unpublished datasets. To
facilitate the research on this task, we build a large and fully open quote
recommendation dataset called QuoteR, which comprises three parts including
English, standard Chinese and classical Chinese. Any part of it is larger than
previous unpublished counterparts. We conduct an extensive evaluation of
existing quote recommendation methods on QuoteR. Furthermore, we propose a new
quote recommendation model that significantly outperforms previous methods on
all three parts of QuoteR. All the code and data of this paper are available at
this https URL.

该研究构建了一个大型的全开放式的引语推荐数据集 QuoteR，并对现有的引语推荐方法进行了全面的评估。此外，他们提出了一种新的引语推荐模型，在所有三个部分的 QuoteR 上均显著优于以前的方法。

QuoteR: 写作中引用推荐的基准评估

QuoteR: A Benchmark of Quote Recommendation for Writing

While composing a new document, anything from a news article to an email or
essay, authors often utilize direct quotes from a variety of sources. Although
an author may know what point they would like to make, selecting an appropriate
quote for the specific context may be time-consuming and difficult. We
therefore propose a novel context-aware quote recommendation system which
utilizes the content an author has already written to generate a ranked list of
quotable paragraphs and spans of tokens from a given source document.
We approach quote recommendation as a variant of open-domain question
answering and adapt the state-of-the-art BERT-based methods from open-QA to our
task. We conduct experiments on a collection of speech transcripts and
associated news articles, evaluating models' paragraph ranking and span
prediction performances. Our experiments confirm the strong performance of
BERT-based methods on this task, which outperform bag-of-words and neural
ranking baselines by more than 30% relative across all ranking metrics.
Qualitative analyses show the difficulty of the paragraph and span
recommendation tasks and confirm the quotability of the best BERT model's
predictions, even if they are not the true selected quotes from the original
news articles.

我们提出了一种新颖的上下文感知引用推荐系统，使用作者已经撰写的内容从给定的源文档中生成可引用的段落和标记跨度的排名列表，通过在演讲转录和新闻文章的集合上进行实验，评估了模型的段落排名和跨度预测性能。