extractive-summarization
extractive-summarizationbertabs-finetuned-extractive-abstractive-summarizationbertabs-finetuned-xsum-extractive-abstractive-summarizationbertabs-finetuned-cnndm-extractive-abstractive-summarizationsimcse-vietnamese-extractive-summarizationusb-extractive_summarization-flant5xlfine-tuned-bert-extractive-summarizationNDC_ExtractiveSummarization_Bert_mitigation_target
long-doc-extractive-summarization-arxivfinancial-reports-extractive-summarization_eval
Financial Reports Extractive Summarization Evaluation Dataset
Validation and test splits for evaluating models on Arabic financial reports extractive summarization.
Dataset Structure
Format: Simple prompt-answer pairs
Validation: ~20 examples (10%)
Test: ~20 examples (10%)
Language: Arabic
Domain: Financial reports and market news
Fields
id: Unique identifier
prompt: The summarization prompt
full_text: Complete financial report
answer: Ground… See the full description on the dataset page: https://huggingface.co/datasets/SahmBenchmark/financial-reports-extractive-summarization_eval.long-doc-extractive-summarization-pubmedlong-doc-extractive-summarization-gov-reportturkish-extractive-summarization-dataset
XTINGE Turkish Extractive Summarization Datasets
This repository hosts three datasets created for advancing Turkish extractive text summarization research: MLSUM_TR_EXT, TES, and XTINGE-SUM_TR_EXT. These datasets are designed to support the development of models capable of generating concise and relevant extractive summaries of Turkish texts.
Below is a Python example showcasing how to download and use these datasets:
from datasets import load_dataset
# Load the MLSUM_TR_EXT… See the full description on the dataset page: https://huggingface.co/datasets/xtinge/turkish-extractive-summarization-dataset.financial-reports-extractive-summarization_train
Financial Reports Extractive Summarization Training Dataset
Training split of the Arabic financial reports extractive summarization dataset in conversational format.
Dataset Structure
Format: Conversational (human-agent pairs)
Size: ~160 training examples (80% of total)
Language: Arabic
Domain: Financial reports and market news
Features
id: Unique identifier
conversations: Human prompt and agent summary
report_type: Type of financial report… See the full description on the dataset page: https://huggingface.co/datasets/SahmBenchmark/financial-reports-extractive-summarization_train.