A Survey of Text Summarization Extractive Techniques

By Vishal Gupta, Gurpreet Singh Lehal. 2010

747 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. Yu-An Chung
2. Michael S. Bernstein
3. Mahak Gambhir
4. Pradip Dhal
5. Chandrashekhar Azad
6. Vasilis Verroios
7. Jie Jessie Ren
8. W. Zhang

In Natural Language Processing

1. Yu-An Chung
2. Jie Jessie Ren
3. W. Zhang
4. Quan Z. Sheng
5. Hu Wang
6. Mingyu Guo
7. Congbo Ma
8. Vasilis Verroios

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).”
• Context Trees: Crowdsourcing Global Understanding from Local Views (2014, methodology): “This framework is similar to extractive summarization in natural language processing (Gupta and Lehal 2010).”
• Clinically Accurate Chest X-Ray Report Generation (2019, methodology): “, 1993) or retrieval-based (Gupta and Lehal, 2010) approaches.”
• Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention (2020, background): “Two key tasks in machine text comprehension are paraphrasing and summarization [8, 28, 9, 41, 24].”
• Recent automatic text summarization techniques: a survey (2016, methodology): “This method employs features of Hindi text summarization system suggested by CDAC Noida as well as Punjabi text summarization system (Gupta and Lehal 2010).”
• Multi-document Summarization via Deep Learning Techniques: A Survey (2020, background): “Most of these works still generate summaries with manually crafted features [108, 158], such as sentence position features [12, 41], sentence length features [41], proper noun features [157], cue-phrase features [59], biased word features, sentence-to-sentence cohesion, and sentenceto-centroid cohes”
• A comprehensive survey on feature selection in the various fields of machine learning (2021, methodology): “Biased word [86] –”
• Text Summarization Techniques: A Brief Survey (2017, background): “Since then, many works have been published to address the problem of automatic text summarization (see [24, 26] for more information about more advanced techniques until 2000s).”