Job descriptions are posted on many online channels, including company
websites, job boards or social media platforms. These descriptions are usually
published with varying text for the same job, due to the requirements of each
platform or to target different audiences. However, for the purpose of
automated recruitment and assistance of people working with these texts, it is
helpful to aggregate job postings across platforms and thus detect duplicate
descriptions that refer to the same job. In this work, we propose an approach
for detecting duplicates in job descriptions. We show that combining
overlap-based character similarity with text embedding and keyword matching
methods lead to convincing results. In particular, we show that although no
approach individually achieves satisfying performance, a combination of string
comparison, deep textual embeddings, and the use of curated weighted lookup
lists for specific skills leads to a significant boost in overall performance.
A tool based on our approach is being used in production and feedback from
real-life use confirms our evaluation.

我们提出了一种用于检测职位描述中的重复内容的方法，结合基于字符相似度的重叠度、文本嵌入和关键词匹配的方法，取得了令人信服的结果。使用我们的方法开发的工具已经在生产中使用，并且来自实际使用的反馈证实了我们的评价。

结合嵌入和领域知识进行职位发布的重复检测

Combining Embeddings and Domain Knowledge for Job Posting Duplicate  Detection

Under the flourishing development in performance, current image-text
retrieval methods suffer from $N$-related time complexity, which hinders their
application in practice. Targeting at efficiency improvement, this paper
presents a simple and effective keyword-guided pre-screening framework for the
image-text retrieval. Specifically, we convert the image and text data into the
keywords and perform the keyword matching across modalities to exclude a large
number of irrelevant gallery samples prior to the retrieval network. For the
keyword prediction, we transfer it into a multi-label classification problem
and propose a multi-task learning scheme by appending the multi-label
classifiers to the image-text retrieval network to achieve a lightweight and
high-performance keyword prediction. For the keyword matching, we introduce the
inverted index in the search engine and create a win-win situation on both time
and space complexities for the pre-screening. Extensive experiments on two
widely-used datasets, i.e., Flickr30K and MS-COCO, verify the effectiveness of
the proposed framework. The proposed framework equipped with only two embedding
layers achieves $O(1)$ querying time complexity, while improving the retrieval
efficiency and keeping its performance, when applied prior to the common
image-text retrieval methods. Our code will be released.

该论文提出了一种关键词指导的预筛选框架来提高图像 - 文本检索的效率，包括关键词匹配和多标签分类方法，并借助倒排索引实现时空复杂度的双赢。经过 Flickr30K 和 MS-COCO 两个广泛使用的数据集的实验证明，该框架能够实现 $O (1)$ 的查询时间复杂度，提高检索效率，而不降低性能。

关键词引导下的高效图像 - 文本检索预筛选

Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening

Generic summaries try to cover an entire document and query-based summaries
try to answer document-specific questions. But real users' needs often fall in
between these extremes and correspond to aspects, high-level topics discussed
among similar types of documents. In this paper, we collect a dataset of
realistic aspect-oriented summaries, AspectNews, which covers different
subtopics about articles in news sub-domains. We annotate data across two
domains of articles, earthquakes and fraud investigations, where each article
is annotated with two distinct summaries focusing on different aspects for each
domain. A system producing a single generic summary cannot concisely satisfy
both aspects. Our focus in evaluation is how well existing techniques can
generalize to these domains without seeing in-domain training data, so we turn
to techniques to construct synthetic training data that have been used in
query-focused summarization work. We compare several training schemes that
differ in how strongly keywords are used and how oracle summaries are
extracted. Our evaluation shows that our final approach yields (a) focused
summaries, better than those from a generic summarization system or from
keyword matching; (b) a system sensitive to the choice of keywords.

本文介绍了一个数据集 AspectNews，并比较了不同训练方案的效果，研究表明最终方法可以生成更好的聚焦摘要，相比通用摘要系统或关键字匹配更为敏感。