Extreme Summarization
11 papers with code • 4 benchmarks • 7 datasets
Image credit: TLDR: Extreme Summarization of Scientific Documents
Libraries
Use these libraries to find Extreme Summarization models and implementations
GEM
XSum
SciTLDR
CiteSum
TLDR9+
SummZoo
Latest papers with no code
ROUGE-K: Do Your Summaries Have Keywords?
Keywords, that is, content-relevant words in summaries play an important role in efficient information conveyance, making it critical to assess if system-generated summaries contain such informative words during evaluation.
Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI
The time needed to answer questions related to the content of abstracts was significantly lower in groups two and three compared to the first group using full abstracts.
Curriculum-guided Abstractive Summarization for Mental Health Online Posts
Automatically generating short summaries from users' online mental health posts could save counselors' reading time and reduce their fatigue so that they can provide timely responses to those seeking help for improving their mental state.
Curriculum-Guided Abstractive Summarization
Recent Transformer-based summarization models have provided a promising approach to abstractive summarization.
IndicBART: A Pre-trained Model for Indic Natural Language Generation
We study pre-trained sequence-to-sequence model for a specific-language family with a focus on Indic languages.
Focus Attention: Promoting Faithfulness and Diversity in Summarization
Professional summaries are written with document-level information, such as the theme of the document, in mind.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
The Effectiveness of Pre-Trained Code Embeddings
Word embeddings are widely used in machine learning based natural language processing systems.
