Building a Hungarian Corpus for Extractive and Abstractive Summarization

Core Concepts

This paper introduces HunSum-2, an open-source Hungarian corpus suitable for training abstractive and extractive summarization models. The dataset is constructed from the Common Crawl corpus, with thorough cleaning, preprocessing, and deduplication. The authors also generate sentence-level labels for extractive summarization using sentence similarity. Baseline models for both extractive and abstractive summarization are trained and evaluated on the dataset.

Abstract

The authors construct an abstractive summarization corpus by performing cleaning and preprocessing on Hungarian segments from the Common Crawl dataset. They also generate an extractive summarization corpus by selecting the most similar article sentence for each lead sentence based on their sentence embeddings.
The key highlights and insights from the paper are:

The authors build an open-source Hungarian corpus, HunSum-2, for training abstractive and extractive summarization models. The dataset is constructed from the Common Crawl corpus, with extensive cleaning, preprocessing, and deduplication.

For extractive summarization, the authors generate sentence-level labels by selecting the most similar article sentence for each lead sentence using sentence embeddings.

The authors train baseline models for both extractive and abstractive summarization using the collected dataset. The extractive model outperforms the abstractive models in terms of ROUGE and BertScore metrics.

The authors conduct a qualitative evaluation, where they find that the abstractive models tend to produce more consistent and grammatically correct summaries, but also have issues with factuality and hallucination.

The dataset, models, and code are publicly available, encouraging replication, further research, and real-world applications across various domains.

Stats

The final preprocessed and deduplicated dataset contains 1.82 million documents.
The average article length is 368.2 tokens and 18.6 sentences, while the average lead length is 27.1 tokens and 1.5 sentences.
The dataset exhibits a Novel N-gram ratio (NNG-1) of 41.12, a compression (CMP) of 89.1, and a redundancy (RED-1) of 11.78.

Quotes

"Training summarization models requires substantial amounts of training data. However for less resourceful languages like Hungarian, openly available models and datasets are notably scarce."
"We train baseline models for both extractive and abstractive summarization using the collected dataset. To demonstrate the effectiveness of the trained models, we perform both quantitative and qualitative evaluation."
"The results show that the mT5 model performs slightly better on all 4 questions. In general, close to 70% of the articles were classified as correctly capturing the gist of the document for both models. Factuality seems to be the biggest pain point as close to two thirds of the generations contained at least one inconsistency with the original article."

Key Insights Distilled From

From News to Summaries

by Boto... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03555.pdf

Deeper Inquiries

How can the dataset and models be further improved to address the issues with factuality and hallucination in the abstractive summaries?

To address the issues with factuality and hallucination in abstractive summaries, several improvements can be made to both the dataset and the models:

Dataset Improvements:

Enhanced Data Cleaning: Implement more robust data cleaning techniques to filter out irrelevant or low-quality articles that may contribute to hallucinations in the summaries.
Fact-Checking Mechanism: Introduce a fact-checking mechanism during dataset creation to verify the accuracy of information in the articles and summaries.
Diverse Data Sources: Include a more diverse range of data sources to provide a broader perspective and reduce bias in the dataset.
Human Annotation: Incorporate human annotation to validate the factual accuracy of the summaries and ensure they align closely with the source text.

Model Enhancements:

Factuality Constraints: Introduce constraints or penalties in the training process to prioritize factuality in the generated summaries.
Knowledge Integration: Incorporate external knowledge bases or fact-checking tools into the model architecture to enhance fact-checking capabilities.
Fine-Tuning Strategies: Explore fine-tuning strategies that specifically target factuality and reduce hallucinations in the generated summaries.
Adversarial Training: Implement adversarial training techniques to encourage the model to generate more factually accurate summaries by penalizing hallucinations.

By implementing these improvements, the dataset and models can be refined to produce more factually accurate and less hallucinatory abstractive summaries.

How can the insights from this work on Hungarian summarization be applied to improve summarization for other low-resource languages?

The insights gained from Hungarian summarization can be extrapolated and applied to enhance summarization tasks in other low-resource languages in the following ways:

Dataset Creation:

Data Collection Strategies: Utilize similar data collection strategies employed in the Hungarian summarization project to gather relevant and diverse data from the web for other low-resource languages.
Preprocessing Techniques: Implement effective preprocessing techniques tailored to the linguistic characteristics of each low-resource language to ensure data quality and relevance.

Model Development:

Transfer Learning: Apply transfer learning techniques using pre-trained multilingual models as a starting point for developing summarization models in low-resource languages.
Fine-Tuning Approaches: Fine-tune existing models on limited data from low-resource languages, leveraging techniques like domain adaptation to improve performance.
Multilingual Training: Explore training models on multilingual datasets to leverage shared linguistic features across languages and enhance summarization capabilities for low-resource languages.

Evaluation and Validation:

Cross-Lingual Evaluation: Conduct cross-lingual evaluation to assess the performance of summarization models across different languages, including low-resource languages, and identify areas for improvement.
Human Evaluation: Incorporate human evaluation to validate the quality and accuracy of summaries generated in low-resource languages, ensuring they capture the essence of the source text effectively.

By applying these insights and methodologies to other low-resource languages, the field of automatic text summarization can be advanced, enabling the development of more effective and accurate summarization models for diverse linguistic contexts.

What other techniques or architectures could be explored to better balance the trade-off between abstractiveness and faithfulness to the source text?

To achieve a better balance between abstractiveness and faithfulness in summarization, several techniques and architectures can be explored:

Copy Mechanisms:

Pointer-Generator Networks: Implement pointer-generator networks to allow the model to copy words directly from the source text, enhancing faithfulness.
Coverage Mechanisms: Introduce coverage mechanisms to track which parts of the source text have been summarized, reducing redundancy and improving faithfulness.

Reinforcement Learning:

Reward Functions: Design reward functions that incentivize the model to generate summaries that are both abstractive and faithful to the source text.
Policy Gradient Methods: Utilize policy gradient methods to optimize the trade-off between abstractiveness and faithfulness during training.

Multi-Task Learning:

Joint Learning Objectives: Incorporate multiple learning objectives, such as maximizing ROUGE scores while minimizing semantic drift, to balance abstractiveness and faithfulness.
Multi-Task Architectures: Explore multi-task architectures that simultaneously optimize for both abstractiveness and faithfulness, leveraging shared representations for improved performance.

Adversarial Training:

Adversarial Regularization: Introduce adversarial training techniques to encourage the model to generate summaries that are faithful to the source text while maintaining abstractiveness.
Discriminative Adversarial Networks: Implement discriminative adversarial networks to distinguish between high-quality summaries that strike a balance between abstractiveness and faithfulness and low-quality summaries.

By exploring these techniques and architectures, researchers can work towards developing summarization models that effectively balance the trade-off between abstractiveness and faithfulness, leading to more coherent and accurate summaries.