Unsupervised Extractive Summarization
11 papers with code • 3 benchmarks • 3 datasets
Neural abstractive summarization models have led to promising results in summarizing relatively short documents.
Unsupervised approaches to extractive summarization usually rely on a notion of sentence importance defined by the semantic similarity between a sentence and the document.
We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research.
Single document summarization has enjoyed renewed interests in recent years thanks to the popularity of neural network models and the availability of large-scale datasets.
We also provide a human-annotated dataset with document-summary pairs to evaluate our abstractive model and to support the comparison of future abstractive summarization systems of the Bengali Language.
Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering
Our explainability study demonstrates the superiority of and preference for summary-level explanations over other explanation types.