An abstractive snippet is an originally created piece of text to summarize a
web page on a search engine results page. Compared to the conventional
extractive snippets, which are generated by extracting phrases and sentences
verbatim from a web page, abstractive snippets circumvent copyright issues;
even more interesting is the fact that they open the door for personalization.
Abstractive snippets have been evaluated as equally powerful in terms of user
acceptance and expressiveness---but the key question remains: Can abstractive
snippets be automatically generated with sufficient quality?
This paper introduces a new approach to abstractive snippet generation: We
identify the first two large-scale sources for distant supervision, namely
anchor contexts and web directories. By mining the entire ClueWeb09 and
ClueWeb12 for anchor contexts and by utilizing the DMOZ Open Directory Project,
we compile the Webis Abstractive Snippet Corpus 2020, comprising more than 3.5
million triples of the form $\langle$query, snippet, document$\rangle$ as
training examples, where the snippet is either an anchor context or a web
directory description in lieu of a genuine query-biased abstractive snippet of
the web document. We propose a bidirectional abstractive snippet generation
model and assess the quality of both our corpus and the generated abstractive
snippets with standard measures, crowdsourcing, and in comparison to the state
of the art. The evaluation shows that our novel data sources along with the
proposed model allow for producing usable query-biased abstractive snippets
while minimizing text reuse.

该论文提出了一种新的生成摘要片段的方法，利用网络目录与锚文本等海量数据编译了一个用于训练抽象式片段生成模型的数据集，并通过标准度量、众包以及与现有技术的比较进行了质量评估，结果表明该方法可生成可用的查询偏向性的抽象式片段且减少文本复用。