SOTAVerified

Text Summarization

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.

Papers

Showing 110 of 1340 papers

TitleStatusHype
LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification0
On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear AttentionCode0
Improving large language models with concept-aware fine-tuningCode1
MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection0
APE: A Data-Centric Benchmark for Efficient LLM Adaptation in Text SummarizationCode0
FiLLM -- A Filipino-optimized Large Language Model based on Southeast Asia Large Language Model (SEALLM)0
Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal LearningCode4
A Structured Literature Review on Traditional Approaches in Current Natural Language Processing0
Multimodal Survival Modeling in the Age of Foundation ModelsCode0
A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource SettingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SelfmemROUGE-150.3Unverified
2BRIOROUGE-149.07Unverified
3PEGASUS + SummaRerankerROUGE-148.12Unverified
4PEGASUS + SimCLSROUGE-147.61Unverified
5PEGASUSLARGEROUGE-147.21Unverified
6HAT-BARTROUGE-145.92Unverified
7BARTROUGE-145.14Unverified
8BertSumExtAbsROUGE-138.81Unverified
9T-ConvS2SROUGE-131.89Unverified
10Baseline : Extractive OracleROUGE-129.79Unverified