Assessing sentence scoring techniques for extractive text summarization
By Rafael Ferreira, Luciano de Souza Cabral, Rafael Dueire Lins, G. Silva, F. Freitas, George D. C. Cavalcanti, Rinaldo Lima, S. Simske, Luciano Favaro. 2013
337 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
- Yu-An Chung
- Mahak Gambhir
- Jie Jessie Ren
- Vishal Gupta
- Peter J. Liu
- James R. Glass
- Hung-yi Lee
- Mohit Bansal
In Natural Language Processing
- Yu-An Chung
- Jie Jessie Ren
- Mahak Gambhir
- Vishal Gupta
- Peter J. Liu
- Mohit Bansal
- Mirella Lapata
- Caiming Xiong
Citations in the corpus
- SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders (2019, background): “Extractive summarization has been studied extensively over the past several decades (Gupta and Lehal, 2010; Ferreira et al., 2013).”
- Recent automatic text summarization techniques: a survey (2016, methodology): “Table 2 continued Summarization approach Need of the approach and their pros and cons Evaluation of sentence scoring methods for extractive summarization of text (Ferreira et al. 2013) Need: This approach needs to be referred to have an insight into the literature in the last decade for getting fami”