20 citations · 70 across the 19 of their papers we have counts for
17 papers · 1 filter
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
Shuyang Cao, Lu Wang
We study generating abstractive summaries that are faithful and factually consistent with the given articles. A novel contrastive learning formulation is presented, which leverages…
Controllable Summarization with Constrained Markov Decision Process
Hou Pong Chan, Lu Wang, Irwin King
We study controllable text summarization which allows users to gain control on a particular attribute (e.g., length limit) of the generated summaries. In this work, we propose a no…
Controllable Open-ended Question Generation with A New Question Type Ontology
Shuyang Cao, Lu Wang
We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences. We first define a new question type ontology which diffe…
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation
Xinyu Hua, Ashwin Sreevatsa, Lu Wang
We study the task of long-form opinion text generation, which faces at least two distinct challenges. First, existing neural generation models fall short of coherence, thus requiri…
Efficient Attentions for Long Document Summarization
Luyang Huang, Shuyang Cao, Nikolaus Parulian +2
The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel e…
Attention Head Masking for Inference Time Content Selection in Abstractive Summarization
Shuyang Cao, Lu Wang
How can we effectively inform content selection in Transformer-based abstractive summarization models? In this work, we present a simple-yet-effective attention head masking techni…