https://github.com/UKPLab/tacl2025-ood-eval-self-rationalization
공식 코드Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related tasks
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.
Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related tasks
Automatic Text Summarization of COVID-19 Medical Research Articles using BERT and GPT-2
Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
Extractive Summarization of Long Documents by Combining Global and Local Context
Systematically Exploring Redundancy Reduction in Summarizing Long Documents
Exploring Explainable Selection to Control Abstractive Summarization
Cross-Lingual Abstractive Summarization with Limited Parallel Resources
LenAtten: An Effective Length Controlling Unit For Text Summarization
Meta-Transfer Learning for Low-Resource Abstractive Summarization
Synchronous Bidirectional Inference for Neural Sequence Generation
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
OpinSummEval: Revisiting Automated Evaluation for Opinion Summarization
An Investigation of Evaluation Metrics for Automated Medical Note Generation
Get To The Point: Summarization with Pointer-Generator Networks
Learning Opinion Summarizers by Selecting Informative Reviews
Beyond Opinion Mining: Summarizing Opinions of Customer Reviews
The Shmoop Corpus: A Dataset of Stories with Loosely Aligned Summaries
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Scheduled Sampling Based on Decoding Steps for Neural Machine Translation
Accelerating Inference in Large Language Models with a Unified Layer Skipping Strategy
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization
Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches
Large Language Models in the Clinic: A Comprehensive Benchmark
Large Language Models Reflect the Ideology of their Creators
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.