https://github.com/makcedward/nlpaug
공식 코드CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in 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.
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization
Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization
Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation
Towards a Unified Multi-Dimensional Evaluator for Text Generation
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization
Factual Error Correction for Abstractive Summarization Models
German also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset
FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization
Evaluating and Improving Factuality in Multimodal Abstractive Summarization
GenerationPrograms: Fine-grained Attribution with Executable Programs
HistAlign: Improving Context Dependency in Language Generation by Aligning with History
Comparative Opinion Summarization via Collaborative Decoding
From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization
Noisy Pairing and Partial Supervision for Stylized Opinion Summarization
Improving large language models with concept-aware fine-tuning
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
MASS: Masked Sequence to Sequence Pre-training for Language Generation
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries
Unified Language Model Pre-training for Natural Language Understanding and Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.