2.7k citations · 3.2k across the 26 of their papers we have counts for
7 papers · 1 filter
Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
Yuning Mao, Xiang Ren, Heng Ji +1
Despite significant progress, state-of-the-art abstractive summarization methods are still prone to hallucinate content inconsistent with the source document. In this paper, we pro…
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning
Yuning Mao, Yanru Qu, Yiqing Xie +2
While neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (M…
Generation-Augmented Retrieval for Open-domain Question Answering
Yuning Mao, Pengcheng He, Xiaodong Liu +4
We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts wi…
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Xin Luna Dong, Xiang He, Andrey Kan +19
Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question a…
Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
Yuning Mao, Tong Zhao, Andrey Kan +4
Taxonomies have found wide applications in various domains, especially online for item categorization, browsing, and search. Despite the prevalent use of online catalog taxonomies,…
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning
Deren Lei, Gangrong Jiang, Xiaotao Gu +3
Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward…