Project Background:
The text summarization development of algorithms and systems capable of condensing large volumes of text into shorter, more concise summaries while retaining the key information and meaning of the original content. Text summarization is crucial in various applications, including information retrieval, document summarization, and content generation. With the exponential growth of digital information, there is an increasing need for automated methods to extract relevant information efficiently. Traditional approaches to text summarization relied on rule-based methods and heuristics, which often struggled to capture the nuances of language and context. However, recent natural language processing and deep learning advancements have paved the way for more sophisticated techniques, including extractive and abstractive summarization methods.
Extractive summarization involves selecting and rearranging existing sentences or passages from the original text, while abstractive summarization involves generating new sentences that capture the essence of the original content. Key objectives in text summarization research include improving the coherence, fluency, and informativeness of summaries, enhancing the ability to handle different text genres and languages, and addressing challenges such as preserving the salience and diversity of information in the summary. Additionally, researchers are exploring techniques for personalized summarization to cater to individual preferences and needs and evaluating the ethical implications of automated summarization systems.
Problem Statement
- Ensure the generated summaries accurately capture the most relevant and important information from the original text while filtering out irrelevant or redundant content.
- Develop techniques to generate informative summaries that capture the key points and main ideas of the original text while preserving the salient details and nuances.
- Investigate methods for text summarization in multiple languages, enabling cross-lingual information processing and accessibility.
- Develop robust evaluation metrics and benchmarks for assessing the quality and effectiveness of text summarization algorithms, ensuring fair and reliable comparisons across different approaches.
- Explore techniques for personalized summarization to cater to individual preferences and needs, adapting the summary content based on user interests, background knowledge, and reading habits.
Aim and Objectives
- Develop automated methods for generating concise summaries from large volumes of text.
- Extract relevant information while filtering out irrelevant content.
- Ensure coherence and readability of generated summaries.
- Preserve the informativeness and salient details of the original text.
- Improve scalability for processing large text datasets efficiently.
- Explore multilingual and cross-lingual summarization techniques.
- Develop robust evaluation metrics for assessing summarization quality.
- Investigate personalized summarization to cater to individual preferences.
Contributions to Text Summarization
- Enhanced coherence and readability of generated summaries.
- Preservation of salient details while condensing content.
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Advancements in scalability for processing large text datasets efficiently.
- Exploration of multilingual and cross-lingual summarization techniques.
- Development of more advanced abstractive summarization capabilities.
- Robust evaluation metrics for assessing summarization quality.
Deep Learning Algorithms for Text Summarization
- Sequence-to-Sequence (Seq2Seq) models
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Recurrent Neural Networks (RNNs)
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Long Short-Term Memory (LSTM) networks
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Convolutional Neural Networks (CNNs)
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Pointer-Generator Networks
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Reinforcement Learning-based models
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Attention Mechanisms
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Transformer Encoders and Decoders
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Hierarchical Attention Networks
Datasets for Text Summarization
- CNN/Daily Mail
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Gigaword
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PubMed
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WikiHow
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Multi-News
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BigPatent
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Reddit TIFU (Today I Fucked Up)
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ArXiv Abstracts
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Scientific Papers
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Legal Texts
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
