activity
20152023
most citedDailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

667 citations · 865 across the 7 of their papers we have counts for

collaborators

5 papers

cs.CL20214 cited

BASS: Boosting Abstractive Summarization with Unified Semantic Graph

Wenhao Wu, Wei Li, Xinyan Xiao +5

Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.…

cs.CL2018

Incorporating Relevant Knowledge in Context Modeling and Response Generation

Yanran Li, Wenjie Li, Ziqiang Cao +1

To sustain engaging conversation, it is critical for chatbots to make good use of relevant knowledge. Equipped with a knowledge base, chatbots are able to extract conversation-rela…

cs.IR2017174 cited

Faithful to the Original: Fact Aware Neural Abstractive Summarization

Ziqiang Cao, Furu Wei, Wenjie Li +1

Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts. Our preliminary study reveals nearly…

cs.CL2017667 cited

DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

Yanran Li, Hui Su, Xiaoyu Shen +3

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset…

cs.CL201518 cited

Multi-Document Summarization via Discriminative Summary Reranking

Xiaojun Wan, Ziqiang Cao, Furu Wei +2

Existing multi-document summarization systems usually rely on a specific summarization model (i.e., a summarization method with a specific parameter setting) to extract summaries f…