Summary of Neural Sequence-to-sequence Modeling with Attention by Leveraging Deep Learning Architectures For Enhanced Contextual Understanding in Abstractive Text Summarization, By Bhavith Chandra Challagundla et al.
Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep Learning Architectures for Enhanced Contextual Understanding in Abstractive Text Summarization
by Bhavith Chandra Challagundla, Chakradhar Peddavenkatagari
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework for automatic text summarization that integrates structural, semantic, and neural-based approaches. The framework is designed to condense large volumes of information into concise summaries, facilitating efficient information retrieval and comprehension. It consists of three main phases: pre-processing, machine learning, and post-processing. In the pre-processing phase, a knowledge-based Word Sense Disambiguation (WSD) technique is employed to generalize ambiguous words, enhancing content generalization. Semantic content generalization is then performed to address out-of-vocabulary (OOV) or rare words, ensuring comprehensive coverage of the input document. The framework uses neural language processing techniques to transform the generalized text into a continuous vector space, and a deep sequence-to-sequence (seq2seq) model with an attention mechanism is employed to predict a generalized summary based on the vector representation. Experimental evaluations conducted on prominent datasets demonstrate the effectiveness of the proposed framework, outperforming existing state-of-the-art deep learning techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to summarize text using computer algorithms. It helps by combining different approaches like understanding word meanings and using neural networks. The method has three steps: preparing the text, using machine learning to create a summary, and refining the summary to make it more readable. This framework can handle rare words and out-of-vocabulary words better than other methods. The results show that this approach works well on different datasets. |
Keywords
» Artificial intelligence » Attention » Deep learning » Generalization » Machine learning » Seq2seq » Summarization » Vector space
