Statistical Features for Extractive Automatic Text Summarization

Automatic Text Summarization (ATS) enables users to save their precious time to retrieve their relevant information need while searching voluminous big data. Text summaries are sensitive to scoring methods, as most of the methods requires to weight features for sentence scoring. In this chapter, various statistical features proposed by researchers for extractive automatic text summarization are explored. Features that perform well are termed as best features using ROUGE evaluation measures and used for creating feature combinations. After that, best performing feature combinations are identified. Performance evaluation of best performing feature combinations on short, medium and large size documents is also conducted using same ROUGE performance measures.

• Publication year

  2020

• Venue

  Natural Language Processing

• Publication date

  Unknown publication date

• Fields of study

  Computer Science

• Identifiers
  DOI 10.4018/978-1-5225-0293-7.CH008
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Extractive single-document summarization based on genetic operators and guided local search

  2014cited by this paper
• Analysis of Sentence Scoring Methods for Extractive Automatic Text Summarization

  2014cited by this paper
• A Context Based Text Summarization System

  2014cited by this paper
• Differential evolution cluster-based text summarization methods

  2013cited by this paper
• SURVEY ON WEB PAGE VISUAL SUMMARIZATION

  2013cited by this paper
• Single Extractive Text Summarization Based on a Genetic Algorithm

  2013cited by this paper
• Condensés de textes par des méthodes numériques

  2012cited by this paper
• A Survey of Text Summarization Techniques

  2012cited by this paper
• Text summarization features selection method using pseudo Genetic-based model

  2012cited by this paper
• Text summarisation in progress: a literature review

  2011cited by this paper
• A New Approach to Improving Multilingual Summarization Using a Genetic Algorithm

  2010cited by this paper
• Implementation and Evaluation of Evolutionary Connectionist Approaches to Automated Text Summarization

  2010cited by this paper
• Simultaneous Ranking and Clustering of Sentences: A Reinforcement Approach to Multi-Document Summarization

  2010cited by this paper
• Fuzzy swarm diversity hybrid model for text summarization

  2010cited by this paper
• A Survey of Text Summarization Extractive Techniques

  2010cited by this paper
• CLUSTERING AND FEATURE SPECIFIC SENTENCE EXTRACTION BASED SUMMARIZATION OF MULTIPLE DOCUMENTS

  2010cited by this paper
• GA, MR, FFNN, PNN and GMM based models for automatic text summarization

  2009cited by this paper
• A new sentence similarity measure and sentence based extractive technique for automatic text summarization

  2009cited by this paper
• Swarm Based Text Summarization

  2009cited by this paper
• A survey of Web clustering engines

  2009cited by this paper
• An Extractive Text Summarizer Based on Significant Words

  2009cited by this paper
• Text Summarization Based on Genetic Programming

  2009cited by this paper
• Extractive Summarization Using Supervised and Semi-Supervised Learning

  2008cited by this paper
• Automatic Text Summarization (The state of the art 2007 and new challenges)

  2008cited by this paper
• Document Summarization Using Conditional Random Fields

  2007cited by this paper
• Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources

  2007cited by this paper
• Web searching, search engines and Information Retrieval

  2005cited by this paper
• ROUGE: A Package for Automatic Evaluation of Summaries

  2004cited by this paper
• Automatic Text Summarization Using a Machine Learning Approach

  2002cited by this paper
• Information Gain Ratio as Term Weight: The case of Summarization of IR Results

  2002cited by this paper
• Summarization System Integrated with Named Entity Tagging and IE pattern Discovery

  2002cited by this paper
• Sentence Extraction System Assembling Multiple Evidence

  2001cited by this paper
• Text summarization via hidden Markov models

  2001cited by this paper
• Experiments in Single and Multi-Document Summarization Using MEAD

  2001cited by this paper
• Advances in Automatic Text Summarization

  1999cited by this paper
• Automatic summarising: factors and directions

  1998cited by this paper
• Dynamic Coreference-Based Summarization

  1998cited by this paper
• Automated Text Summarization and the Summarist System

  1998cited by this paper
• Improving summarization through rhetorical parsing tuning

  1998cited by this paper
• Using Lexical Chains for Text Summarization

  1997cited by this paper
• Automatic Text Structuring and Summarization

  1997cited by this paper
• An algorithm for suffix stripping

  1997cited by this paper
• Identifying Topics by Position

  1997cited by this paper
• Inverse Document Frequency (IDF): A Measure of Deviations from Poisson

  1995cited by this paper
• A trainable document summarizer

  1995cited by this paper
• Automatic Condensation of Electronic Publications by Sentence Selection

  1995cited by this paper
• Abstract Generation Based on Rhetorical Structure Extraction

  1994cited by this paper
• Extended Boolean information retrieval

  1983cited by this paper
• Automatic Abstracting Research at Chemical Abstracts Service

  1975cited by this paper
• Automatic abstracting and indexing. II. Production of indicative abstracts by application of contextual inference and syntactic coherence criteria

  1971cited by this paper
• New Methods in Automatic Extracting

  1969cited by this paper
• Machine-Made Index for Technical Literature - An Experiment

  1958cited by this paper
• The Automatic Creation of Literature Abstracts

  1958cited by this paper
• Sentence Extraction Based Single Document Summarization

  year unknowncited by this paper

• Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation

  2023cites this paper
• Particle Swarm Optimization for Punjabi Text Summarization

  2021cites this paper