20 citations · 80 across the 28 of their papers we have counts for
8 papers · 1 filter
An Entity-Driven Framework for Abstractive Summarization
Eva Sharma, Luyang Huang, Zhe Hu +1
Abstractive summarization systems aim to produce more coherent and concise summaries than their extractive counterparts. Popular neural models have achieved impressive results for…
Sentence-Level Content Planning and Style Specification for Neural Text Generation
Xinyu Hua, Lu Wang
Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as sepa…
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization
Hai Ye, Wenjie Li, Lu Wang
Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing a NL descript…
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
Hou Pong Chan, Wang Chen, Lu Wang +1
Generating keyphrases that summarize the main points of a document is a fundamental task in natural language processing. Although existing generative models are capable of predicti…
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
Eva Sharma, Chen Li, Lu Wang
Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure. In such datasets, summary-worthy content often ap…
Argument Generation with Retrieval, Planning, and Realization
Xinyu Hua, Zhe Hu, Lu Wang
Automatic argument generation is an appealing but challenging task. In this paper, we study the specific problem of counter-argument generation, and present a novel framework, CAND…