8 citations · 20 across the 9 of their papers we have counts for
9 papers
A New Approach to Overgenerating and Scoring Abstractive Summaries
Kaiqiang Song, Bingqing Wang, Zhe Feng +1
We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users' ne…
CATE: Computation-aware Neural Architecture Encoding with Transformers
Shen Yan, Kaiqiang Song, Fei Liu +1
Recent works (White et al., 2020a; Yan et al., 2020) demonstrate the importance of architecture encodings in Neural Architecture Search (NAS). These encodings encode either structu…
Automatic Summarization of Open-Domain Podcast Episodes
Kaiqiang Song, Chen Li, Xiaoyang Wang +2
We present implementation details of our abstractive summarizers that achieve competitive results on the Podcast Summarization task of TREC 2020. A concise textual summary that cap…
Better Highlighting: Creating Sub-Sentence Summary Highlights
Sangwoo Cho, Kaiqiang Song, Chen Li +3
Amongst the best means to summarize is highlighting. In this paper, we aim to generate summary highlights to be overlaid on the original documents to make it easier for readers to…
Controlling the Amount of Verbatim Copying in Abstractive Summarization
Kaiqiang Song, Bingqing Wang, Zhe Feng +2
An abstract must not change the meaning of the original text. A single most effective way to achieve that is to increase the amount of copying while still allowing for text abstrac…
Joint Parsing and Generation for Abstractive Summarization
Kaiqiang Song, Logan Lebanoff, Qipeng Guo +5
Sentences produced by abstractive summarization systems can be ungrammatical and fail to preserve the original meanings, despite being locally fluent. In this paper we propose to r…