Text Summarization Techniques: A Brief Survey (1707.02268v3)

Abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

• The paper presents a comprehensive review of text summarization methods, emphasizing both extractive and abstractive approaches.
• It details key techniques such as TFIDF, latent semantic analysis, and Bayesian models for creating intermediate text representations.
• The survey underscores the need for advanced summarization tools and highlights future research directions leveraging deep learning.

An Overview of Text Summarization Techniques: A Survey

The paper, "Text Summarization Techniques: A Brief Survey," authored by Mehdi Allahyari et al., presents a comprehensive review of automatic text summarization methods. This survey delineates the proliferation of textual data and the ensuing requirement for effective summarization to render information digestible and manageable.

Introduction to Text Summarization

Automatic text summarization aims to produce concise summaries that encapsulate the essential information of the original documents. The paper distinguishes between extractive and abstractive summarization. Extractive methods select key sentences directly from the source, while abstractive techniques generate new phrases to convey the core message. Although abstractive summarization aligns more closely with human summary creation, extractive methods are currently predominant due to the complexities of natural language understanding required by abstractive approaches.

Extractive Summarization Process

The paper outlines three fundamental tasks in extractive summarization: constructing an intermediate representation of the text, assigning scores to sentences, and selecting the most critical sentences for the summary. Intermediate representations can be categorized into topic representation and indicator representation.

Topic Representation Approaches

1. Topic Words: The use of frequency-based methods such as word likelihood and TFIDF to determine the importance of words in summarization is discussed. The centroid-based summarization using TFIDF is highlighted as a prevalent technique.
2. Latent Semantic Analysis (LSA): LSA approaches are utilized for identifying semantic topics within a document, which facilitates the selection of representative sentences.
3. Bayesian Topic Models: This section explores the use of models like Latent Dirichlet Allocation (LDA) that interpret the thematic structure of texts, improving the probabilistic scoring of sentences.

Integrating Knowledge Bases

The paper recognizes the utility of combining summarization techniques with knowledge bases to enhance semantic understanding and output quality. The integration of domain-specific ontologies can improve the selection of relevant content.

Contextual Influence in Summarization

The authors discuss the influence of external context, such as citations in scientific articles or comments in blogs, as additional sources for enhancing summarization quality. This contextual information can highlight significant segments of the original text that merit inclusion in the summary.

Indicator Representation Approaches

Indicator-based methods employ features to rank sentences, often leveraging graph-based and machine learning techniques for more nuanced analysis and selection.

Evaluation Metrics

The evaluation of summarization quality is nontrivial, with the challenge of identifying optimal summaries accentuated by the subjective nature of "importance" and "informativeness." The paper references the ROUGE metric as the predominant automatic evaluation technique, which compares n-grams between candidate and reference summaries for assessing quality.

Conclusions and Future Directions

The paper concludes by emphasizing the pressing need for automatic summarization tools in managing the voluminous information generated online. There is potential for future advancements in abstractive methods, leveraging deep learning to address inherent challenges in semantic interpretation and language generation.

Implications and Future Research

The implications of this survey are significant for both practical applications and theoretical advancements in NLP. Future research may focus on enhancing abstractive techniques, improving the integration of knowledge bases, and refining evaluation metrics to better capture summary quality.

In summary, this paper provides an insightful and detailed analysis of current text summarization techniques, charting a path for future exploration and development in this essential field.