Multi-Objective Ant Colony Optimization (MOACO) Approach for Multi-Document Text Summarization †
Abstract
:1. Introduction
2. Related Works
3. Proposed Framework
3.1. Text Preprocessing
- Sentence Segmentation: The objective of separating each sentence in the document collection is to determine its beginning and end.
- Word Tokenization: The phrases’ words are retrieved one by one. Any exclamation, question, and other marks are removed from the text.
- Stop Word Removal: Stop words, which include “prepositions, conjunctions, articles, possessives, pronouns, and others,” are frequently used words that have no particular meaning. As a result, these lines are removed from the sentences. A list of 598 stop words in the English language is contained in the ROUGE tool.
- Word Stemming: The roots of the remaining words are then retrieved using the Porter stemming technique, which allows words with the same lexical root to be processed as a single word. As one of the most frequently used and expanded algorithms, the Porter stemming algorithm has emerged as the industry standard for word conflation for information retrieval across a wide range of languages.
3.2. Word Embedding
3.3. Redundancy Reduction
3.4. Multi-Objective Ant Colony Optimization (MOACO)
4. The Proposed Multi-Objective Ant Colony Optimization Framework
4.1. Problem Representation
4.2. Pheromone Representation
4.3. The Solution Representation Algorithm
5. Experimental Results and Discussion
5.1. Dataset Description
5.2. ROUGH Evaluation Metrics
5.3. MOACO with Baselines Evaluation Metrics
5.4. Convergence Analysis based on MOACO
5.5. Case Study
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Feature Description | DUC 2005 | DUC 2006 | DUC 2007 |
---|---|---|---|
Number of Topics | 50 | 50 | 50 |
Number of documents per topic | 25 to 50 | 35 | 30 |
Total number of documents | 1682 | 1130 | 1150 |
Data source | TREC | AQUAINT | AQUAINT |
Reference summary length | 200 words | 250 words | 300 words |
t Metrics | DUC 2005 | DUC 2006 | DUC 2007 | ||||||
---|---|---|---|---|---|---|---|---|---|
Rough-A | Rough-B | Rough-Z | Rough-A | Rough-B | Rough-Z | Rough-A | Rough-B | Rough-Z | |
Recall | 0.401 | 0.089 | 0.136 | 0.451 | 0.1 | 0.175 | 0.527 | 0.110 | 0.157 |
Precision | 0.395 | 0.081 | 0.140 | 0.463 | 0.156 | 0.169 | 0.5 | 0.097 | 0.155 |
F-Score | 0.408 | 0.085 | 0.143 | 0.468 | 0.113 | 0.178 | 0.523 | 0.099 | 0.156 |
System | ROUGH-A | ROUGH-B | ROUGH-Z |
---|---|---|---|
D-ACO | 0.795 | 0.409 | 0.121 |
MACS | 0.804 | 0.415 | 0.130 |
ABC | 0.815 | 0.456 | 0.139 |
MOACO | 0.829 | 0.459 | 0.147 |
ABC | 0.815 | 0.456 | 0.139 |
MOACO | 0.829 | 0.459 | 0.147 |
System | ROUGH-A | ROUGH-B | ROUGH-Z |
---|---|---|---|
D-ACO | 0.831 | 0.493 | 0.163 |
MACS | 0.876 | 0.502 | 0.175 |
ABC | 0.883 | 0.525 | 0.179 |
MOACO | 0.915 | 0.531 | 0.185 |
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Muddada, M.K.; Vankara, J.; Nandini, S.S.; Karetla, G.R.; Naidu, K.S. Multi-Objective Ant Colony Optimization (MOACO) Approach for Multi-Document Text Summarization. Eng. Proc. 2023, 59, 218. https://doi.org/10.3390/engproc2023059218
Muddada MK, Vankara J, Nandini SS, Karetla GR, Naidu KS. Multi-Objective Ant Colony Optimization (MOACO) Approach for Multi-Document Text Summarization. Engineering Proceedings. 2023; 59(1):218. https://doi.org/10.3390/engproc2023059218
Chicago/Turabian StyleMuddada, Murali Krishna, Jayavani Vankara, Sekharamahanti S. Nandini, Girija Rani Karetla, and Kaparapu Sowjanya Naidu. 2023. "Multi-Objective Ant Colony Optimization (MOACO) Approach for Multi-Document Text Summarization" Engineering Proceedings 59, no. 1: 218. https://doi.org/10.3390/engproc2023059218