Pointer over Attention: An Improved Bangla Text Summarization Approach Using Hybrid Pointer Generator Network

TL;DR

This paper introduces a hybrid pointer generator network with coverage mechanism to improve Bangla text summarization, reducing repetition and enhancing factual accuracy, outperforming previous models on large datasets.

Contribution

The paper presents a novel hybrid pointer generator model with coverage for Bangla summarization, achieving state-of-the-art results and demonstrating stability on large-scale datasets.

Findings

Achieved ROUGE-1 score of 0.66 on BANSData

Achieved ROUGE-2 score of 0.42 on BANS-133k

Surpassed previous Bengali summarization techniques

Abstract

Despite the success of the neural sequence-to-sequence model for abstractive text summarization, it has a few shortcomings, such as repeating inaccurate factual details and tending to repeat themselves. We propose a hybrid pointer generator network to solve the shortcomings of reproducing factual details inadequately and phrase repetition. We augment the attention-based sequence-to-sequence using a hybrid pointer generator network that can generate Out-of-Vocabulary words and enhance accuracy in reproducing authentic details and a coverage mechanism that discourages repetition. It produces a reasonable-sized output text that preserves the conceptual integrity and factual information of the input article. For evaluation, we primarily employed "BANSData" - a highly adopted publicly available Bengali dataset. Additionally, we prepared a large-scale dataset called "BANS-133" which consists of 133k Bangla news articles associated with human-generated summaries. Experimenting with the proposed model, we achieved ROUGE-1 and ROUGE-2 scores of 0.66, 0.41 for the "BANSData" dataset and 0.67, 0.42 for the BANS-133k" dataset, respectively. We demonstrated that the proposed system surpasses previous state-of-the-art Bengali abstractive summarization techniques and its stability on a larger dataset. "BANS-133" datasets and code-base will be publicly available for research.