Using K-Means and Variable Neighborhood Search for Automatic Summarization of Scientific Articles

Akhmetov, Iskander; Mladenović, Nenad; Mussabayev, Rustam

doi:10.1007/978-3-030-69625-2_13

Cited by 5 publications

(8 citation statements)

References 17 publications

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“…In [2] we experimented with using a supervised method to build a binary classification model for sentences, which is something similar to what was suggested by Kupiec in 1995 [17], as we got labels using the VNS method. However, this time e also got labels produced by the Greedy algorithm and failed to build a good classification model.…”

Section: B Summarization Methodsmentioning

confidence: 99%

“…The inverse document frequency (IDF) measures how informative the word is and how common or rare it is among other documents. It is the logarithmically scaled inverse fraction of the documents that contain the word given in (2).…”

Section: ) Variable Neighborhood Search (Vns)mentioning

confidence: 99%

“…In our previous article [2] we approached the task of finding the best possible ROUGE-1 score using only VNS heuristics. However, this time for comparison, we also used a greedy algorithm and added the ROUGE-2 score.…”

Section: Experiments a Attaining Best Achievable Rouge Scorementioning

confidence: 99%

“…The ROUGE score results distributions shown in Figure 8 and Figure 9 are dense, and allow to assume that the approach is working steadily as expected. For examples of the summaries produced by Greedy Summarizer on arXive dataset please visit the link 2 .…”

Section: ) Greedy Extractive Summarization Qualitymentioning

confidence: 99%

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Greedy Optimization Method for Extractive Summarization of Scientific Articles

Akhmetov¹,

Gelbukh

Mussabayev

2021

IEEE Access

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This work presents a method for summarizing scientific articles from the arXive and PubMed datasets using a greedy Extractive Summarization algorithm. We used the approach along with Variable Neighborhood Search (VNS) to learn what is the top-line exists in the area of Extractive Text Summarization quality in terms of ROUGE scores. The algorithm is based on first selecting for the summary the sentences from the text containing the maximum number of words with the higher TFIDF values along with minimum document frequency parameter tuning for TFIDF vectorization. As a result, the method achieves 0.43/0.12 and 0.40/0.13 for ROUGE-1/ROUGE-2 scores on arXive and PubMed datasets, respectively. These results are comparable to the state-of-the-art models using complex neural network architectures and serious computational resources together with the large amounts of training data. In contrast, our method uses a straightforward statistical inference methodology.

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Section: B Summarization Methodsmentioning

confidence: 99%

Section: ) Variable Neighborhood Search (Vns)mentioning

confidence: 99%

Section: Experiments a Attaining Best Achievable Rouge Scorementioning

confidence: 99%

Section: ) Greedy Extractive Summarization Qualitymentioning

confidence: 99%

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Greedy Optimization Method for Extractive Summarization of Scientific Articles

Akhmetov¹,

Gelbukh

Mussabayev

2021

IEEE Access

Self Cite

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“…The goal of cluster analysis is to organize a collection of points into subsets, called clusters, by similarity [1,2]. Cluster analysis has a large variety of applications in different areas, including information retrieval, pattern recognition and classification, network analysis, vector quantization and data compression, data mining and knowledge discovery, document clustering and image processing and segmentation [4,5,33].…”

Section: Introduction 1problem Formulationmentioning

confidence: 99%

Big-means: Less is More for K-means Clustering

Mussabayev¹,

Mladenović²,

Jarboui³

et al. 2022

Preprint

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K-means clustering plays a vital role in data mining. However, its performance drastically drops when applied to huge amounts of data. We propose a new heuristic that is built on the basis of regular K-means for faster and more accurate big data clustering using the "less is more" and MSSC decomposition approaches. The main advantage of the proposed algorithm is that it naturally turns the K-means local search into global one through the process of decomposition of the MSSC problem. On one hand, decomposition of the MSSC problem into smaller subproblems reduces the computational complexity and allows for their parallel processing. On the other hand, the MSSC decomposition provides a new method for the natural data-driven shaking of the incumbent solution while introducing a new neighborhood structure for the solution of the MSSC problem. This leads to a new heuristic that improves K-means in big data conditions. The scalability of the algorithm to big data can be easily adjusted by choosing the appropriate number of subproblems and their size. The proposed algorithm is both scalable and accurate. In our experiments it outperforms all recent state-of-the-art algorithms for the MSSC in terms of time as well as the solution quality.

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