From Extractive to Abstractive Summarization: A Journey

By Parth Mehta, Prasenjit Majumder. 2019

42 citations recorded by the source corpus.

The availability of large documentsummary corpora have opened up new possibilities for using statistical text generation techniques for abstractive summarization. Progress in Extractive text summarization has become stagnant for a while now and in this work we compare the two possible alternates to it. We present an argument in favor of

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. Caiming Xiong
4. Christopher D. Manning
5. Mirella Lapata
6. Furu Wei
7. Jianfeng Gao
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 (Saggion and Poibeau, 2013; Mehta, 2016).”