Keyphrases are capable of providing semantic metadata characterizing
documents and producing an overview of the content of a document. Since
keyphrase extraction is able to facilitate the management, categorization, and
retrieval of information, it has received much attention in recent years. There
are three approaches to address keyphrase extraction: (i) traditional two-step
ranking method, (ii) sequence labeling and (iii) generation using neural
networks. Two-step ranking approach is based on feature engineering, which is
labor intensive and domain dependent. Sequence labeling is not able to tackle
overlapping phrases. Generation methods (i.e., Sequence-to-sequence neural
network models) overcome those shortcomings, so they have been widely studied
and gain state-of-the-art performance. However, generation methods can not
utilize context information effectively. In this paper, we propose a novelty
Span Keyphrase Extraction model that extracts span-based feature representation
of keyphrase directly from all the content tokens. In this way, our model
obtains representation for each keyphrase and further learns to capture the
interaction between keyphrases in one document to get better ranking results.
In addition, with the help of tokens, our model is able to extract overlapped
keyphrases. Experimental results on the benchmark datasets show that our
proposed model outperforms the existing methods by a large margin.

该论文提出了一种新颖的 Span Keyphrase Extraction 模型，能够直接从所有内容令牌中提取以跨度为基础的关键短语的特征表示，并进一步学习捕捉单个文档中关键短语之间的交互，以获得更好的排序结果。

基于跨度特征表示的关键短语提取

Keyphrase Extraction with Span-based Feature Representations

One key challenge in talent search is how to translate complex criteria of a
hiring position into a search query. This typically requires deep knowledge on
which skills are typically needed for the position, what are their
alternatives, which companies are likely to have such candidates, etc. However,
listing examples of suitable candidates for a given position is a relatively
easy job. Therefore, in order to help searchers overcome this challenge, we
design a next generation of talent search paradigm at LinkedIn: Search by Ideal
Candidates. This new system only needs the searcher to input one or several
examples of suitable candidates for the position. The system will generate a
query based on the input candidates and then retrieve and rank results based on
the query as well as the input candidates. The query is also shown to the
searcher to make the system transparent and to allow the searcher to interact
with it. As the searcher modifies the initial query and makes it deviate from
the ideal candidates, the search ranking function dynamically adjusts an
refreshes the ranking results balancing between the roles of query and ideal
candidates. As of writing this paper, the new system is being launched to our
customers.

在 LinkedIn 中，我们设计了一种新的人才搜索范例，名为 “理想候选人搜索”，只需要用户输入一到多个合适候选人的示例，系统将基于输入的候选人生成一个查询并检索和排名结果。