10 citations · 35 across the 12 of their papers we have counts for
12 papers · 1 filter
CiteEval: Principle-Driven Citation Evaluation for Source Attribution
Yumo Xu, Peng Qi, Jifan Chen +7
Citation quality is crucial in information-seeking systems, directly influencing trust and the effectiveness of information access. Current evaluation frameworks, both human and au…
Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models
Weijia Zhang, Jia-Hong Huang, Svitlana Vakulenko +3
Query-focused summarization (QFS) is a fundamental task in natural language processing with broad applications, including search engines and report generation. However, traditional…
Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Tasks
Huajian Zhang, Yumo Xu, Laura Perez-Beltrachini
We study existing approaches to leverage off-the-shelf Natural Language Inference (NLI) models for the evaluation of summary faithfulness and argue that these are sub-optimal due t…
QTSumm: Query-Focused Summarization over Tabular Data
Yilun Zhao, Zhenting Qi, Linyong Nan +9
People primarily consult tables to conduct data analysis or answer specific questions. Text generation systems that can provide accurate table summaries tailored to users' informat…
Text Summarization with Oracle Expectation
Yumo Xu, Mirella Lapata
Extractive summarization produces summaries by identifying and concatenating the most important sentences in a document. Since most summarization datasets do not come with gold lab…
Tackling Query-Focused Summarization as A Knowledge-Intensive Task: A Pilot Study
Weijia Zhang, Svitlana Vakulenko, Thilina Rajapakse +2
Query-focused summarization (QFS) requires generating a summary given a query using a set of relevant documents. However, such relevant documents should be annotated manually and t…
Text Summarization with Latent Queries
Yumo Xu, Mirella Lapata
The availability of large-scale datasets has driven the development of neural models that create summaries from single documents, for generic purposes. When using a summarization s…
Generating Query Focused Summaries from Query-Free Resources
Yumo Xu, Mirella Lapata
The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents. In this work we consider query…
Meta Dialogue Policy Learning
Yumo Xu, Chenguang Zhu, Baolin Peng +1
Dialog policy determines the next-step actions for agents and hence is central to a dialogue system. However, when migrated to novel domains with little data, a policy model can fa…
Query Focused Multi-Document Summarization with Distant Supervision
Yumo Xu, Mirella Lapata
We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization (QFS). Due to the lack of training data, existing wor…
Bootstrapping a Crosslingual Semantic Parser
Tom Sherborne, Yumo Xu, Mirella Lapata
Recent progress in semantic parsing scarcely considers languages other than English but professional translation can be prohibitively expensive. We adapt a semantic parser trained…
Weakly Supervised Domain Detection
Yumo Xu, Mirella Lapata
In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual segments which are domain-heavy, i.e., sentences…