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20152021
most citedA Joint Model for Question Answering and Question Generation

83 citations · 149 across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.CL20211 cited

Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents

Rui Meng, Khushboo Thaker, Lei Zhang +4

Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured…

cs.CL2020

An Empirical Study on Neural Keyphrase Generation

Rui Meng, Xingdi Yuan, Tong Wang +3

Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. Mode…

cs.CL2020

Exploring and Predicting Transferability across NLP Tasks

Tu Vu, Tong Wang, Tsendsuren Munkhdalai +5

Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. Can fine-tuning these models on tasks other…

cs.CL2018

One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases

Xingdi Yuan, Tong Wang, Rui Meng +4

Different texts shall by nature correspond to different number of keyphrases. This desideratum is largely missing from existing neural keyphrase generation models. In this study, w…

cs.CL201783 cited

A Joint Model for Question Answering and Question Generation

Tong Wang, Xingdi Yuan, Adam Trischler

We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequence-to-sequence framework tha…