15 citations · 32 across the 9 of their papers we have counts for
7 papers · 1 filter
Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness
Dan Su
In this thesis, we investigated the relevance, faithfulness, and succinctness aspects of Long Form Question Answering (LFQA). LFQA aims to generate an in-depth, paragraph-length an…
Read before Generate! Faithful Long Form Question Answering with Machine Reading
Dan Su, Xiaoguang Li, Jindi Zhang +4
Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question. While current work on LFQA using large pre-trained model for generation are eff…
QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation
Dan Su, Peng Xu, Pascale Fung
Multi-hop question generation (MQG) aims to generate complex questions which require reasoning over multiple pieces of information of the input passage. Most existing work on MQG h…
Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance
Dan Su, Tiezheng Yu, Pascale Fung
Query focused summarization (QFS) models aim to generate summaries from source documents that can answer the given query. Most previous work on QFS only considers the query relevan…
Dimsum @LaySumm 20: BART-based Approach for Scientific Document Summarization
Tiezheng Yu, Dan Su, Wenliang Dai +1
Lay summarization aims to generate lay summaries of scientific papers automatically. It is an essential task that can increase the relevance of science for all of society. In this…
Multi-hop Question Generation with Graph Convolutional Network
Dan Su, Yan Xu, Wenliang Dai +3
Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more c…