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A text processing framework that applies Encoder-Decoder architecture with attention mechanism functions as the main focus of this research for resolving Natural Language Processing predictive problems. The research first outlines base technologies along with methodologies and frameworks required to build the system design. Detailed analysis of the dataset happens at this phase through observing dataset structure and calculating statistical summaries to detect missing or duplicate values. Better understanding of the dataset by using descriptive analytics to identify potential problems which leads them to improve the dataset. Data cleaning serves multiple functions during the process by eliminating unneeded columns together with missing value management and text normalization methods. The normalization procedure entails converting text into either upper or lowercase format and executes tag stripping alongside URL replacement and shorthand elimination and emoji and contraction removal. The processing begins after tokenization divides the text into segments. After cleaning the data the input and output components get separated while padding is used to maintain consistent dimensional structure. The model base incorporates an Encoder-Decoder framework combined with attention functionality while implementing a BiLSTM network. Through this specific model configuration both past and future inputs can be read contextually which boosts the prediction accuracy. The designed model produces 90.23 percent achievement in accuracy which highlights its strong capability in processing intricate NLP operations.

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A Sequence-to-Sequence Approach for Text Summarization Using Bi-LSTM Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Amulya Naik,
  • Pallavi Dhaded,
  • Shireesh Hakki,
  • Satish Chikkamath,
  • Suneeta V. Budihal,
  • Sujata Kotabagi

摘要

A text processing framework that applies Encoder-Decoder architecture with attention mechanism functions as the main focus of this research for resolving Natural Language Processing predictive problems. The research first outlines base technologies along with methodologies and frameworks required to build the system design. Detailed analysis of the dataset happens at this phase through observing dataset structure and calculating statistical summaries to detect missing or duplicate values. Better understanding of the dataset by using descriptive analytics to identify potential problems which leads them to improve the dataset. Data cleaning serves multiple functions during the process by eliminating unneeded columns together with missing value management and text normalization methods. The normalization procedure entails converting text into either upper or lowercase format and executes tag stripping alongside URL replacement and shorthand elimination and emoji and contraction removal. The processing begins after tokenization divides the text into segments. After cleaning the data the input and output components get separated while padding is used to maintain consistent dimensional structure. The model base incorporates an Encoder-Decoder framework combined with attention functionality while implementing a BiLSTM network. Through this specific model configuration both past and future inputs can be read contextually which boosts the prediction accuracy. The designed model produces 90.23 percent achievement in accuracy which highlights its strong capability in processing intricate NLP operations.