https://github.com/webis-de/eacl24-tldr-progress
공식 코드TL;DR Progress: Multi-faceted Literature Exploration in Text 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.
TL;DR Progress: Multi-faceted Literature Exploration in Text Summarization
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization
Summary Explorer: Visualizing the State of the Art in Text Summarization
Summary Workbench: Unifying Application and Evaluation of Text Summarization Models
Personalized Abstractive Summarization by Tri-agent Generation Pipeline
Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency
Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization
Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization
Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers
Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization
ODSum: New Benchmarks for Open Domain Multi-Document Summarization
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization
Global-aware Beam Search for Neural Abstractive Summarization
Global-aware Beam Search for Neural Abstractive Summarization
Abstractive Summarization Guided by Latent Hierarchical Document Structure
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
Iterative Document Representation Learning Towards Summarization with Polishing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.