1.2k citations · 3.4k across the 22 of their papers we have counts for
22 papers
Improving Sentence Similarity Estimation for Unsupervised Extractive Summarization
Shichao Sun, Ruifeng Yuan, Wenjie Li +1
Unsupervised extractive summarization aims to extract salient sentences from a document as the summary without labeled data. Recent literatures mostly research how to leverage sent…
Few-shot Query-Focused Summarization with Prefix-Merging
Ruifeng Yuan, Zili Wang, Ziqiang Cao +1
Query-focused summarization has been considered as an important extension for text summarization. It aims to generate a concise highlight for a given query. Different from text sum…
Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT
Ruifeng Yuan, Zili Wang, Wenjie Li
Most current extractive summarization models generate summaries by selecting salient sentences. However, one of the problems with sentence-level extractive summarization is that th…
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Hongwei Wang, Fuzheng Zhang, Mengdi Zhang +4
Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could…
When Collaborative Filtering Meets Reinforcement Learning
Yu Lei, Wenjie Li
In this paper, we study a multi-step interactive recommendation problem, where the item recommended at current step may affect the quality of future recommendations. To address the…
Knowledge Graph Convolutional Networks for Recommender Systems
Hongwei Wang, Miao Zhao, Xing Xie +2
To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and desi…
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
Hongwei Wang, Fuzheng Zhang, Miao Zhao +3
Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to a…
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks
Chenchen Li, Jialin Wang, Hongwei Wang +3
User emotion analysis toward videos is to automatically recognize the general emotional status of viewers from the multimedia content embedded in the online video stream. Existing…
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation
Hui Su, Xiaoyu Shen, Wenjie Li +1
Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems. Though highly efficient in learning the backbone of huma…
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
Jingjing Xu, Xu Sun, Qi Zeng +4
The goal of sentiment-to-sentiment "translation" is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To…
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Shuming Ma, Xu Sun, Wei Li +3
Most recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the tra…
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
Hongwei Wang, Fuzheng Zhang, Jialin Wang +4
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve…
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
Xu Sun, Weiwei Sun, Shuming Ma +4
Recent systems on structured prediction focus on increasing the level of structural dependencies within the model. However, our study suggests that complex structures entail high o…
Faithful to the Original: Fact Aware Neural Abstractive Summarization
Ziqiang Cao, Furu Wei, Wenjie Li +1
Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts. Our preliminary study reveals nearly…
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
Yanran Li, Hui Su, Xiaoyu Shen +3
We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset…
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization
Shuming Ma, Xu Sun, Jingjing Xu +3
Current Chinese social media text summarization models are based on an encoder-decoder framework. Although its generated summaries are similar to source texts literally, they have…
Improving Multi-Document Summarization via Text Classification
Ziqiang Cao, Wenjie Li, Sujian Li +1
Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents. Text classification just…
Joint Copying and Restricted Generation for Paraphrase
Ziqiang Cao, Chuwei Luo, Wenjie Li +1
Many natural language generation tasks, such as abstractive summarization and text simplification, are paraphrase-orientated. In these tasks, copying and rewriting are two main wri…
AttSum: Joint Learning of Focusing and Summarization with Neural Attention
Ziqiang Cao, Wenjie Li, Sujian Li +2
Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization. Previous supervised summarization systems often perform the…
TGSum: Build Tweet Guided Multi-Document Summarization Dataset
Ziqiang Cao, Chengyao Chen, Wenjie Li +3
The development of summarization research has been significantly hampered by the costly acquisition of reference summaries. This paper proposes an effective way to automatically co…
Component-Enhanced Chinese Character Embeddings
Yanran Li, Wenjie Li, Fei Sun +1
Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English. In this wor…
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines
Xiaozhao Zhao, Yuexian Hou, Dawei Song +1
Typical dimensionality reduction (DR) methods are often data-oriented, focusing on directly reducing the number of random variables (features) while retaining the maximal variation…