Query-aware webpage snippet extraction is widely used in search engines to
help users better understand the content of the returned webpages before
clicking. Although important, it is very rarely studied. In this paper, we
propose an effective query-aware webpage snippet extraction method named
DeepQSE, aiming to select a few sentences which can best summarize the webpage
content in the context of input query. DeepQSE first learns query-aware
sentence representations for each sentence to capture the fine-grained
relevance between query and sentence, and then learns document-aware
query-sentence relevance representations for snippet extraction. Since the
query and each sentence are jointly modeled in DeepQSE, its online inference
may be slow. Thus, we further propose an efficient version of DeepQSE, named
Efficient-DeepQSE, which can significantly improve the inference speed of
DeepQSE without affecting its performance. The core idea of Efficient-DeepQSE
is to decompose the query-aware snippet extraction task into two stages, i.e.,
a coarse-grained candidate sentence selection stage where sentence
representations can be cached, and a fine-grained relevance modeling stage.
Experiments on two real-world datasets validate the effectiveness and
efficiency of our methods.

DeepQSE 提出了一种有效的查询感知网页片段提取方法，其核心思想是将查询感知的片段提取任务分解为两个阶段，并建立查询感知的句子表示，经过实验验证，该方法具有效率和有效性。