Machine Learning-Based System for Automated Presentation Generation from CSV Data Article Swipe
Balusamy Nachiappan
,
N. Rajkumar
,
C. Kalpana
,
A. Mohanraj
,
B Prabhu Shankar
,
C. Viji
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.56294/dm2024359
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.56294/dm2024359
Effective presentation slides are crucial for conveying information efficiently, yet existing tools lack content analysis capabilities. This paper introduces a content-based PowerPoint presentation generator, aiming to address this gap. By leveraging automated techniques, slides are generated from text documents, ensuring original concepts are effectively communicated. Unstructured data poses challenges for organizations, impacting productivity and profitability. While traditional methods fall short, AI-based approaches offer promise. This systematic literature review (SLR) explores AI methods for extracting data from unstructured details. Findings reveal limitations in existing methods, particularly in handling complex document layouts. Moreover, publicly available datasets are task-specific and of low quality, highlighting the need for comprehensive datasets reflecting real-world scenarios. The SLR underscores the potential of Artificial-based approaches for information extraction but emphasizes the challenges in processing diverse document layouts. The proposed is a framework for constructing high-quality datasets and advocating for closer collaboration between businesses and researchers to address unstructured data challenges effectively
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- https://doi.org/10.56294/dm2024359
- OA Status
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All OpenAlex metadata
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https://openalex.org/W4400307885Canonical identifier for this work in OpenAlex
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https://doi.org/10.56294/dm2024359Digital Object Identifier
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Machine Learning-Based System for Automated Presentation Generation from CSV DataWork title
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2024Year of publication
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Balusamy Nachiappan, N. Rajkumar, C. Kalpana, A. Mohanraj, B Prabhu Shankar, C. VijiList of authors in order
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https://doi.org/10.56294/dm2024359Publisher landing page
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4Total citation count in OpenAlex
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2025: 2, 2024: 2Per-year citation counts (last 5 years)
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| abstract_inverted_index.reflecting | 106 |
| abstract_inverted_index.scenarios. | 108 |
| abstract_inverted_index.systematic | 65 |
| abstract_inverted_index.effectively | 43, 152 |
| abstract_inverted_index.information | 7, 118 |
| abstract_inverted_index.limitations | 80 |
| abstract_inverted_index.researchers | 146 |
| abstract_inverted_index.techniques, | 32 |
| abstract_inverted_index.traditional | 56 |
| abstract_inverted_index.underscores | 111 |
| abstract_inverted_index.Unstructured | 45 |
| abstract_inverted_index.constructing | 135 |
| abstract_inverted_index.efficiently, | 8 |
| abstract_inverted_index.high-quality | 136 |
| abstract_inverted_index.highlighting | 100 |
| abstract_inverted_index.particularly | 84 |
| abstract_inverted_index.presentation | 1, 22 |
| abstract_inverted_index.productivity | 52 |
| abstract_inverted_index.unstructured | 76, 149 |
| abstract_inverted_index.capabilities. | 15 |
| abstract_inverted_index.collaboration | 142 |
| abstract_inverted_index.communicated. | 44 |
| abstract_inverted_index.comprehensive | 104 |
| abstract_inverted_index.content-based | 20 |
| abstract_inverted_index.task-specific | 95 |
| abstract_inverted_index.organizations, | 50 |
| abstract_inverted_index.profitability. | 54 |
| abstract_inverted_index.Artificial-based | 115 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.82306294 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |