Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters
Enriching the Transformers with Structured Representations for Abstractive Summarization
Extending Multi-Document Summarization Evaluation to the Interactive Setting
Sliding Selector Network with Dynamic Memory for Extractive Summarization of Long Documents
What’s in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization
D2S  Automated slide generation with query-based text summarization from documents
Understanding Factuality in Abstractive Summarization with FRANK  A Benchmark for Factuality Metrics
QMSum  A New Benchmark for Query-based Multi-domain Dialogue Summarization
Predicting Discourse Trees from Transformer-based Neural Summarizers
RefSum  Refactoring Neural Summarization
Noisy Self-Knowledge Distillation for Text Summarization
GSum  A General Framework for Guided Neural Abstractive Summarization
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation
MediaSum  A Large-scale Media Interview Dataset for Dialogue Summarization
Enhancing Factual Consistency of Abstractive Summarization
Structure-Aware Abstractive Dialogue Summarization via Discourse and Action Graphs
AdaptSum  Towards Low-Resource Domain Adaptation for Abstractive Summarization
A New Approach to Overgenerating and Scoring Abstractive Summaries
Attention Head Masking for Inference Time Content Selection in Abstractive Summarization
Inference Time Style Control for Summarization
Efficient Attentions for Long Document Summarization
Nutribullets Hybrid  Multi-document Health Summarization
MM-AVS  A Full-Scale Dataset for Multi-modal Summarization
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection
