Extractive Text Summarization
31 papers with code • 4 benchmarks • 5 datasets
Given a document, selecting a subset of the words or sentences which best represents a summary of the document.
These leaderboards are used to track progress in Extractive Text Summarization
LibrariesUse these libraries to find Extractive Text Summarization models and implementations
Most implemented papers
Get To The Point: Summarization with Pointer-Generator Networks
Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).
Text Summarization with Pretrained Encoders
For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not).
Efficient Attention: Attention with Linear Complexities
Dot-product attention has wide applications in computer vision and natural language processing.
Fine-tune BERT for Extractive Summarization
BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks.
Leveraging BERT for Extractive Text Summarization on Lectures
This paper reports on the project called Lecture Summarization Service, a python based RESTful service that utilizes the BERT model for text embeddings and KMeans clustering to identify sentences closes to the centroid for summary selection.
A Neural Attention Model for Abstractive Sentence Summarization
Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build.
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step.
DebateSum: A large-scale argument mining and summarization dataset
Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today.
Centroid-based Text Summarization through Compositionality of Word Embeddings
The textual similarity is a crucial aspect for many extractive text summarization methods.
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc.