https://github.com/yixinL7/SimCLS
공식 코드SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form.
There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
On Learning to Summarize with Large Language Models as References
Evaluating the Factual Consistency of Abstractive Text Summarization
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness
EDU-level Extractive Summarization with Varying Summary Lengths
Boosting Summarization with Normalizing Flows and Aggressive Training
Non-Autoregressive Text Generation with Pre-trained Language Models
QAPyramid: Fine-grained Evaluation of Content Selection for Text Summarization
Weakly-Supervised Opinion Summarization by Leveraging External Information
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
PartialFormer: Modeling Part Instead of Whole for Machine Translation
Correction with Backtracking Reduces Hallucination in Summarization
Empowering Diffusion Models on the Embedding Space for Text Generation
Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts
Differentiable N-gram Objective on Abstractive Summarization
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems
AIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generation
Unsupervised Sentence Compression using Denoising Auto-Encoders
Reducing Quantity Hallucinations in Abstractive Summarization
To Adapt or to Fine-tune: A Case Study on Abstractive Summarization
Muppet: Massive Multi-task Representations with Pre-Finetuning
Muppet: Massive Multi-task Representations with Pre-Finetuning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.