Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Multiview Convolutional Neural Networks for Multidocument Extractive Summarization
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network
Reader-Aware Multi-Document Summarization via Sparse Coding
Combining syntax and semantics for automatic extractive single-document summarization
A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks
Modeling Document Summarization as Multi-objective Optimization
Multi-Document Summarization via Discriminative Summary Reranking
Multi-Document Summarization by Maximizing Informative Content-Words
Abstract Meaning Representation for Multi-Document Summarization
Ranking with recursive neural networks and its application to multi-document summarization
Exploring Text Links for Coherent Multi-Document Summarization
Abstractive Multi-Document Summarization via Phrase Selection and Merging
Unsupervised Query-Focused Multi-Document Summarization using the Cross Entropy Method
Extractive Summarization using Deep Learning
Meaning Representation for Multi-Document Summarization
Optimizing an Approximation of ROUGE - a Problem-Reduction Approach to Extractive Multi-Document Summarization
FastSum: Fast and Accurate Query-based Multi-document Summarization
Extractive Text Summarization Using Word Vector Embedding
High Performance Convolutional Neural Networks for Document Processing
Extractive Multi-Document Summarization Using population-based Multicriteria Optimization
Aerie: flexible file-system interfaces to storage-class memory
The Role of Syntax in Vector Space Models of Compositional Semantics
Pricing Cloud Compute Commodities: A Novel Financial Economic Model
Check my chart: a robust color chart tracker for colorimetric camera calibration
Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages