Automatic Text Summarization Using a Machine Learning Approach

By Joel Larocca Neto, Alex Alves Freitas, Celso A. A. Kaestner. 2002

262 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. Caiming Xiong
2. Mohit Bansal
3. Richard Socher
4. Jianfeng Gao
5. Graham Neubig
6. Yejin Choi
7. Percy Liang
8. Luke S. Zettlemoyer

In Natural Language Processing

1. Caiming Xiong
2. Richard Socher
3. Mohit Bansal
4. Graham Neubig
5. Mirella Lapata
6. Bryan McCann
7. Christopher D. Manning
8. Jianfeng Gao

Citations in the corpus

• A Deep Reinforced Model for Abstractive Summarization (2017, methodology): “Most summarization models studied in the past are extractive in nature (Neto et al., 2002; Dorr et al., 2003; Filippova and Altun, 2013; Col-menares et al., 2015; Nallapati et al., 2017 A well-studied set of summarization tasks is the Document Understanding Conference (DUC) .”
• Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond (2016, background): “A vast majority of the past work in summarization has been extractive, which consists of identifying key sentences or passages in the source document and reproducing them as summary (Neto et al., 2002; Erkan and Radev, 2004; Wong et al., 2008; Filippova and Altun, 2013; Colmenares et al., 2015).”
• Neural Text Summarization: A Critical Evaluation (2019, background): “Non-neural approaches (Neto et al., 2002; Dorr et al., 2003; Filippova and Altun, 2013; Colmenares et al., 2015) utilized domain expertise to develop heuristics for summary content selection, whereas more recent, neural techniques allow for end-to-end training.”
• Improving Abstraction in Text Summarization (2018, methodology): “…the model selects passages from the input document and combines them to form a shorter summary, sometimes with a post-processing step to ensure final coherence of the output (Neto et al., 2002; Dorr et al., 2003; Filippova and Altun, 2013; Colmenares et al., 2015; Nallapati et al., 2017).”
• Abstract Text Summarization with a Convolutional Seq2seq Model (2019, background): “Lots of previous work in summarization focuses on extractive methods, that is, directly extract key words or sentences from the original document and then reproduce them as summary [12–15].”
• Efficient Summarization with Read-Again and Copy Mechanism (2016, background): “Notable examples are Neto et al. (2002), Erkan & Radev (2004), Wong et al. (2008), Filippova & Altun (2013) and Colmenares et al. (2015).”