Abstract. When writing an academic paper, researchers often spend
considerable time reviewing and summarizing papers to extract relevant
citations and data to compose the Introduction and Related Work sections. To
address this problem, we propose QuOTeS, an interactive system designed to
retrieve sentences related to a summary of the research from a collection of
potential references and hence assist in the composition of new papers. QuOTeS
integrates techniques from Query-Focused Extractive Summarization and
High-Recall Information Retrieval to provide Interactive Query-Focused
Summarization of scientific documents. To measure the performance of our
system, we carried out a comprehensive user study where participants uploaded
papers related to their research and evaluated the system in terms of its
usability and the quality of the summaries it produces. The results show that
QuOTeS provides a positive user experience and consistently provides
query-focused summaries that are relevant, concise, and complete. We share the
code of our system and the novel Query-Focused Summarization dataset collected
during our experiments at this https URL

介绍了 QuOTeS，一种交互式系统，它集成了 Query-Focused Extractive Summarization 和 High-Recall Information Retrieval 技术，旨在从潜在的引用文献集合中检索与研究摘要相关的句子，帮助撰写新论文，同时经过全面的用户研究表明，该系统能够提供积极的用户体验和相关的、简明、完整的摘要。

基于查询的技术摘要： QuOTeS

QuOTeS: Query-Oriented Technical Summarization

Quotes are critical for establishing credibility in news articles. A direct
quote enclosed in quotation marks has a strong visual appeal and is a sign of a
reliable citation. Unfortunately, this journalistic practice is not strictly
followed, and a quote in the headline is often "contextomized." Such a quote
uses words out of context in a way that alters the speaker's intention so that
there is no semantically matching quote in the body text. We present QuoteCSE,
a contrastive learning framework that represents the embedding of news quotes
based on domain-driven positive and negative samples to identify such an
editorial strategy. The dataset and code are available at
this https URL.

QuoteCSE 是一种对新闻文章引用的嵌入式对比学习框架，它基于正负样本的域驱动来识别新闻标题中的语境引用，从而提高报导的可信度。

通过对比学习检测新闻标题中的上下文引用

Detecting Contextomized Quotes in News Headlines by Contrastive Learning

Quotes of public figures can mark turning points in history. A quote can
explain its originator's actions, foreshadowing political or personal decisions
and revealing character traits. Impactful quotes cross language barriers and
influence the general population's reaction to specific stances, always facing
the risk of being misattributed or taken out of context. The provision of a
cross-lingual knowledge graph of quotes that establishes the authenticity of
quotes and their contexts is of great importance to allow the exploration of
the lives of important people as well as topics from the perspective of what
was actually said. In this paper, we present QuoteKG, the first multilingual
knowledge graph of quotes. We propose the QuoteKG creation pipeline that
extracts quotes from Wikiquote, a free and collaboratively created collection
of quotes in many languages, and aligns different mentions of the same quote.
QuoteKG includes nearly one million quotes in $55$ languages, said by more than
$69,000$ people of public interest across a wide range of topics. QuoteKG is
publicly available and can be accessed via a SPARQL endpoint.

本文介绍了 QuoteKG ，即第一个多语言名言知识图谱，该图谱通过从 Wikiquote 中提取名言，并将不同的引用对齐来建立名言及其上下文的真实性，包括了来自 $55$ 种语言的将近一百万条名言，由 $69,000$ 多位公众人物说出，覆盖了广泛的主题，并通过 SPARQL 端点公开提供。