https://github.com/hooshvare/pn-summary
공식 코드Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization
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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.
Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization
DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization
HEGEL: Hypergraph Transformer for Long Document Summarization
An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics
Personalized Language Modeling from Personalized Human Feedback
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models
EMO-KNOW: A Large Scale Dataset on Emotion and Emotion-cause
From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews
Learning by Semantic Similarity Makes Abstractive Summarization Better
Inducing Causal Structure for Abstractive Text Summarization
Russian-Language Multimodal Dataset for Automatic Summarization of Scientific Papers
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
IOT: Instance-wise Layer Reordering for Transformer Structures
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order
Unsupervised Opinion Summarization as Copycat-Review Generation
VerbCL: A Dataset of Verbatim Quotes for Highlight Extraction in Case Law
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization
Multi-Dimensional Evaluation of Text Summarization with In-Context Learning
GUMSum: Multi-Genre Data and Evaluation for English Abstractive Summarization
Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis
Behavioral Analysis of Information Salience in Large Language Models
Balancing Lexical and Semantic Quality in Abstractive Summarization
Neural Extractive Text Summarization with Syntactic Compression
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution
Understanding Neural Abstractive Summarization Models via Uncertainty
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.