23 citations · 64 across the 20 of their papers we have counts for
19 papers · 1 filter
Zero-shot Faithful Factual Error Correction
Kung-Hsiang Huang, Hou Pong Chan, Heng Ji
Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge bases and preventing hallucinations in sequence-to-sequence models. Drawing on h…
Measuring the Effect of Influential Messages on Varying Personas
Chenkai Sun, Jinning Li, Hou Pong Chan +2
Predicting how a user responds to news events enables important applications such as allowing intelligent agents or content producers to estimate the effect on different communitie…
Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization
Hou Pong Chan, Qi Zeng, Heng Ji
Existing factual consistency evaluation approaches for text summarization provide binary predictions and limited insights into the weakness of summarization systems. Therefore, we…
Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation
Qi Zeng, Mankeerat Sidhu, Ansel Blume +3
Opinions in scientific research papers can be divergent, leading to controversies among reviewers. However, most existing datasets for opinion summarization are centered around pro…
ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media
Kung-Hsiang Huang, Hou Pong Chan, Kathleen McKeown +1
Considerable advancements have been made to tackle the misrepresentation of information derived from reference articles in the domains of fact-checking and faithful summarization.…
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
Chi Seng Cheang, Hou Pong Chan, Derek F. Wong +5
Recent pre-trained language models (PLMs) achieve promising results in existing abstractive summarization datasets. However, existing summarization benchmarks overlap in time with…
SumREN: Summarizing Reported Speech about Events in News
Revanth Gangi Reddy, Heba Elfardy, Hou Pong Chan +2
A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i.e., the 5 Ws; Who,…
MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective
Zhe Hu, Hou Pong Chan, Lifu Huang
Teaching neural models to generate narrative coherent texts is a critical problem. Recent pre-trained language models have achieved promising results, but there is still a gap betw…
Multimedia Generative Script Learning for Task Planning
Qingyun Wang, Manling Li, Hou Pong Chan +4
Goal-oriented generative script learning aims to generate subsequent steps to reach a particular goal, which is an essential task to assist robots or humans in performing stereotyp…
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
Zhe Hu, Hou Pong Chan, Jiachen Liu +3
Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that re…
Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward
Zhe Hu, Zhiwei Cao, Hou Pong Chan +4
Controllable text generation is an appealing but challenging task, which allows users to specify particular attributes of the generated outputs. In this paper, we propose a control…
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…
Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization
Wang Chen, Piji Li, Hou Pong Chan +1
Dialogue summarization aims to generate a summary that indicates the key points of a given dialogue. In this work, we propose an end-to-end neural model for dialogue summarization…
A Condense-then-Select Strategy for Text Summarization
Hou Pong Chan, Irwin King
Select-then-compress is a popular hybrid, framework for text summarization due to its high efficiency. This framework first selects salient sentences and then independently condens…
A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss
Hou Pong Chan, Wang Chen, Irwin King
Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary…
Exclusive Hierarchical Decoding for Deep Keyphrase Generation
Wang Chen, Hou Pong Chan, Piji Li +1
Keyphrase generation (KG) aims to summarize the main ideas of a document into a set of keyphrases. A new setting is recently introduced into this problem, in which, given a documen…
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…
Topic-Aware Neural Keyphrase Generation for Social Media Language
Yue Wang, Jing Li, Hou Pong Chan +3
A huge volume of user-generated content is daily produced on social media. To facilitate automatic language understanding, we study keyphrase prediction, distilling salient informa…
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction
Wang Chen, Hou Pong Chan, Piji Li +2
In this paper, we present a novel integrated approach for keyphrase generation (KG). Unlike previous works which are purely extractive or generative, we first propose a new multi-t…