Automatic generic document summarization based on non-negative matrix factorization

By Ju-hong Lee, Sun Park, Chan-Min Ahn, Daeho Kim. 2009

163 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nijat R. Isazade
2. Rasim M. Alguliyev
3. Benno Stein
4. Shahbaz Syed
5. Michael Völske
6. Ramiz M. Aliguliyev
7. Martin Potthast
8. Mahak Gambhir

In Natural Language Processing

1. Benno Stein
2. Shahbaz Syed
3. Nijat R. Isazade
4. Michael Völske
5. Ramiz M. Aliguliyev
6. Martin Potthast
7. Rasim M. Alguliyev
8. Kyunghyun Cho

Citations in the corpus

• TL;DR: Mining Reddit to Learn Automatic Summarization (2017, methodology): “Generally considered too small to train abstractive summarization systems, past research has focused on the use of various optimization methods—such as non-negative matrix factorization (Lee et al., 2009), support vector regression (Ouyang et al.”
• Dialect Diversity in Text Summarization on Twitter (2020, unknown): “[40] Ju-Hong Lee, Sun Park, Chan-Min Ahn, and Daeho Kim.”
• Multiple documents summarization based on evolutionary optimization algorithm (2013, methodology): “NMF (Lee et al., 2009): The method performs NMF on terms by sentences matrix and then ranks the sentences by their weighted scores.”
• Pseudo-Relevance Feedback Based on Matrix Factorization (2016, unknown): “, document clustering [2], document summarization [17], and probabilistic latent semantic indexing [9].”