• Publications
  • Influence
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
TLDR
A high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects, the language is human-written and less noisy and the dialogues reflect the authors' daily communication way and cover various topics about their daily life. Expand
Faithful to the Original: Fact Aware Neural Abstractive Summarization
TLDR
This work argues that faithfulness is also a vital prerequisite for a practical abstractive summarization system and proposes a dual-attention sequence-to-sequence framework to force the generation conditioned on both the source text and the extracted fact descriptions. Expand
Text-level Discourse Dependency Parsing
TLDR
The state-of-the-art dependency parsing techniques, the Eisner algorithm and maximum spanning tree (MST) algorithm, are adopted to parse an optimal discourse dependency tree based on the arcfactored model and the large-margin learning techniques. Expand
Ranking with Recursive Neural Networks and Its Application to Multi-Document Summarization
We develop a Ranking framework upon Recursive Neural Networks (R2N2) to rank sentences for multi-document summarization. It formulates the sentence ranking task as a hierarchical regression process,Expand
Learning Summary Prior Representation for Extractive Summarization
TLDR
A novel summary system called PriorSum is developed, which applies the enhanced convolutional neural networks to capture the summary prior features derived from length-variable phrases under a regression framework, and concatenated with document-dependent features for sentence ranking. Expand
A Novel Neural Topic Model and Its Supervised Extension
TLDR
A novel neural topic model (NTM) where the representation of words and documents are efficiently and naturally combined into a uniform framework and is competitive in both topic discovery and classification/regression tasks. Expand
Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization
TLDR
This paper uses a popular IR platform to use existing summaries as soft templates to guide the seq2seq model, and extends the framework to jointly conduct template Reranking and template-aware summary generation (Rewriting). Expand
PolyU at CL-SciSumm 2016
TLDR
This work presents a meta-modelling architecture that automates the very labor-intensive and therefore time-heavy and expensive and expensive process of manually cataloging and processing text for retrieval and annotation. Expand
AttSum: Joint Learning of Focusing and Summarization with Neural Attention
TLDR
A novel summarization system called AttSum is proposed, which automatically learns distributed representations for sentences as well as the document cluster and applies the attention mechanism to simulate the attentive reading of human behavior when a query is given. Expand
TGSum: Build Tweet Guided Multi-Document Summarization Dataset
TLDR
This paper proposes an effective way to automatically collect large scales of news-related multi-document summaries with reference to social media's reactions by utilizing two types of social labels in tweets, i.e., hashtags and hyper-links. Expand
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