Comparative Study Between Two Swarm Intelligence Automatic Text Summaries: Social Spiders vs Social Bees

@article{Boudia2018ComparativeSB,
  title={Comparative Study Between Two Swarm Intelligence Automatic Text Summaries: Social Spiders vs Social Bees},
  author={Amine Boudia and Reda Mohamed Hamou and Abdelmalek Amine},
  journal={Int. J. Appl. Metaheuristic Comput.},
  year={2018},
  volume={9},
  pages={15-39}
}
This article is a comparative study between two bio-inspired approach based on the swarm intelligence for automatic text summaries: Social Spiders and Social Bees. The authors use two techniques of... 

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

TLDR
A new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization, and shows that using user labeled information in the training set helps to find better metrics and weights.

Using Hybrid Classifiers to Conduct Intangible Assets Evaluation

TLDR
Using data-mining technologies to identify important valuation factors and to determine an optimal valuation model, the results show that decision trees have approximately 75% prediction accuracy and select seven critical variables.

References

SHOWING 1-10 OF 26 REFERENCES

The Social Spiders in the Clustering of Texts: Towards an Aspect of Visual Classification

TLDR
A new biomimetic approach based on social spiders to solve a combinatorial problem ie the automatic classification of texts because a very large data stream flows and particularly on the web.

A New Meta-Heuristic Based on Social Bees for Detection and Filtering of Spam

TLDR
The authors took inspiration from biological model of social bees and especially, their organization in the workplace, and collective intelligence to create a meta-heuristic that will allow the authors to detect the characteristics of unwanted data.

Hybridization of Social Spiders and Extractions Techniques for Automatic Text Summaries

TLDR
A new multilayer approach for automatic text summaries using two techniques of extraction, one after the other: scoring of phrases, and similarity that aims to eliminate redundant phrases without losing the theme of the text.

A New Biomimetic Method Based on the Power Saves of Social Bees for Automatic Summaries of Texts by Extraction

TLDR
A new approach for automatic text summarization by extraction by extraction based on Saving Energy Function where the first step constitute to use two techniques of extraction: scoring of phrases, and similarity that aims to eliminate redundant phrases without losing the theme of the text.

A New Biomimetic Approach Based on Social Spiders for Clustering of Text

TLDR
To validate the classification the authors used a measure of assessment based on recall and precision (f-measure) and a language-independent method was used to represent text documents is that of n-grams characters and words.

Hybridization Between Scoring Technique and Similarity Technique for Automatic Summarization by Extraction

TLDR
The authors will give a short state of art that will allow them later to explain the weakness and strength of each technique, after that they will explain their approach of Hybridization.

A New Approach Based on the Detection of Opinion by SentiWordNet for Automatic Text Summaries by Extraction

TLDR
A new approach based on the detection of opinion by the SentiWordNet for the production of text summarization by using the scoring extraction technique adapted to detecting of opinion is proposed.

A Comparison of Rankings Produced by Summarization Evaluation Measures

TLDR
This paper proposes using sentence-rank-based and content-based measures for evaluating extract summaries, and compares these with recall-based evaluation measures.

Graph-Based Keyword Extraction for Single-Document Summarization

TLDR
The experiments show that given a set of summarized training documents, the supervised classification provides the highest keyword identification accuracy, while the highest F-measure is reached with a simple degree-based ranking.