[Paper Review] Automatic Keyword Extraction for Text Summarization: A Survey

Santosh Kumar Bharti, Korra Sathya Babu|arXiv (Cornell University)|Apr 11, 2017

Advanced Text Analysis Techniques80 references77 citations

TL;DR

This paper surveys the literature on automatic keyword extraction and its role in text summarization, covering methodologies, databases, evaluations, challenges, and future directions.

ABSTRACT

In recent times, data is growing rapidly in every domain such as news, social media, banking, education, etc. Due to the excessiveness of data, there is a need of automatic summarizer which will be capable to summarize the data especially textual data in original document without losing any critical purposes. Text summarization is emerged as an important research area in recent past. In this regard, review of existing work on text summarization process is useful for carrying out further research. In this paper, recent literature on automatic keyword extraction and text summarization are presented since text summarization process is highly depend on keyword extraction. This literature includes the discussion about different methodology used for keyword extraction and text summarization. It also discusses about different databases used for text summarization in several domains along with evaluation matrices. Finally, it discusses briefly about issues and research challenges faced by researchers along with future direction.

Motivation & Objective

• Motivate the need for automatic summarization due to rapid data growth across domains.
• Review existing work on keyword extraction and its critical link to effective text summarization.
• Summarize methodologies used for keyword extraction and assess associated evaluation metrics and datasets.
• Discuss issues, research challenges, and potential directions for future work in this area.

Proposed method

• Survey a broad range of literature on keyword extraction techniques and their application to text summarization.
• Discuss methodological categories used for keyword extraction and how they feed into summarization.
• Summarize data sources and databases used for evaluation across different domains.
• Outline common evaluation matrices and criteria for assessing keyword extraction effectiveness.

Experimental results

Research questions

• RQ1 What are the prevailing keyword extraction methods used for text summarization?
• RQ2 What datasets and evaluation metrics are commonly employed in this research area?
• RQ3 What challenges and open questions remain in automatic keyword extraction for summarization?
• RQ4 What future directions are suggested to advance research in this field.

Key findings

• The literature demonstrates a strong dependence of summarization quality on effective keyword extraction.
• Multiple methodological approaches are used, with varying applicability across domains.
• There exist several databases and domain-specific corpora used for evaluation of keyword extraction in summarization.
• Evaluation metrics and benchmarks are diverse, highlighting a need for standardized assessment.
• The paper identifies practical challenges and outlines future research directions to improve robustness and applicability.