Automatic Text Summarization: Past, Present and Future

By Horacio Saggion, Thierry Poibeau. 2013

178 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. Mohit Bansal
2. Peter J. Liu
3. Jianfeng Gao
4. Caiming Xiong
5. Christopher D. Manning
6. Mirella Lapata
7. Furu Wei
8. Richard Socher

In Natural Language Processing

1. Mohit Bansal
2. Peter J. Liu
3. Mirella Lapata
4. Richard Socher
5. Caiming Xiong
6. Christopher D. Manning
7. Furu Wei
8. Jianfeng Gao

Citations in the corpus

• Get To The Point: Summarization with Pointer-Generator Networks (2017, background): “Due to the difficulty of abstractive summarization, the great majority of past work has been extractive (Kupiec et al., 1995; Paice, 1990; Saggion and Poibeau, 2013).”
• Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts (2018, methodology): “Extractive methods assemble the summary from the source text directly [1, 2, 3], while abstractive methods generate words to form the summary [4, 5, 6].”
• Text Summarization Techniques: A Brief Survey (2017, background): “There have been several evaluation campaigns since the late 1990s in the US [58].”