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Paragraph-level attention based deep model for chapter segmentation.

Paveen Virameteekul¹

¹Department of Computer Science & Engineering, Shanghai Jiao Tong University, Minhang, Shanghai, China.

Peerj. Computer Science

|June 20, 2022

Summary

This summary is machine-generated.

This study introduces a new deep learning method for automatic book chapter segmentation. The novel approach uses paragraph-level semantics and an attention mechanism, achieving superior performance over existing state-of-the-art models.

Keywords:

BERT Convolutional neural network Machine learning Natural language processing Neural networks Supervised learning Text segmentation XLNet

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Area of Science:

Natural Language Processing
Computational Linguistics
Artificial Intelligence

Background:

Automatic segmentation of long texts, such as books, into chapters is a challenging task due to the semi-structured nature of the data.
Existing deep learning models often overlook paragraph-level semantic relationships crucial for accurate chapter boundary detection.

Purpose of the Study:

To propose a novel deep learning-based method for book chapter segmentation that incorporates paragraph-level semantics and an attention mechanism.
To improve the accuracy and efficiency of automatic chapter segmentation in digital texts.

Main Methods:

Utilized a pre-trained XLNet model integrated with a convolutional neural network (CNN) to extract semantic features from individual paragraphs.
Developed an attention mechanism to leverage paragraph-level semantic similarities for predicting chapter boundaries.
Employed public datasets for training and evaluating the proposed chapter segmentation model.

Main Results:

The proposed method achieved a high F1 score of 0.8856, significantly outperforming the Bidirectional Encoder Representations from Transformers (BERT) model (F1 score of 0.6640).
Ablation studies confirmed the substantial performance improvement attributed to the paragraph-level attention mechanism.
Demonstrated state-of-the-art (SOTA) performance in book chapter segmentation tasks on public datasets.

Conclusions:

The novel deep learning approach effectively segments book chapters by utilizing paragraph-level semantics and an attention mechanism.
The proposed method offers a significant advancement over existing techniques for automatic text segmentation.
Incorporating paragraph-level semantic understanding is vital for enhancing the performance of chapter segmentation models.