Relevance-Based Headline Classification and Generation Dataset for Telugu News Articles

Core Concepts

Developing a high-quality, human-annotated dataset for relevance-based classification of Telugu news headlines, and demonstrating its impact on improving headline generation models.

Abstract

The authors present "TeClass", a novel dataset for relevance-based headline classification in the Telugu language. The dataset contains 26,178 article-headline pairs, annotated by human annotators into three categories: Highly Related (HREL), Moderately Related (MREL), and Least Related (LREL).
The authors conduct comprehensive experiments using various baseline models, including traditional machine learning approaches and state-of-the-art BERT-based models. The results show that the BERT-based models, particularly mDeBERTa, outperform the classical machine learning models, achieving an F1 weighted score of 0.63 and an F1 macro score of 0.64.
Furthermore, the authors demonstrate the impact of the TeClass dataset on improving headline generation models. They fine-tune an mT5 model on different subsets of the dataset and observe a significant improvement in ROUGE-L scores (around 5 points) when the model is trained on highly relevant article-headline pairs compared to the non-fine-tuned model.
The authors emphasize the importance of high-quality, relevance-based data for headline generation tasks, as the presence of irrelevant headlines can negatively impact the performance of such models. The TeClass dataset and the annotation guidelines are made publicly available to encourage future research in this area.

Stats

The average number of sentences in the articles is around 10.
The average number of tokens in the articles is around 126.
The average number of tokens in the headlines is around 6.

Quotes

"Relevance-based headline classification can greatly aid the task of generating relevant headlines."
"The headlines generated by the models fine-tuned on highly relevant article-headline pairs, showed about a 5 point increment in the ROUGE-L scores."

Key Insights Distilled From

TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu

by Gopichand Ka... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11349.pdf

Deeper Inquiries

How can the TeClass dataset be extended to other low-resource languages beyond Telugu?

To extend the TeClass dataset to other low-resource languages, a systematic approach can be followed. Firstly, identifying similar news websites in the target languages and scraping article-headline pairs from these sources would be essential. Custom site-specific web scrapers, similar to the ones developed for TeClass, can be tailored to each news website to ensure accurate extraction of data. Crowd-sourcing can then be utilized for the annotation process, engaging native speakers of the target languages to assign relevance categories to the article-headline pairs. The annotation guidelines developed for TeClass can serve as a reference for annotators working on other languages. By following a similar methodology and leveraging the expertise gained from creating TeClass, datasets for other low-resource languages can be systematically developed.

What are the potential challenges in applying the relevance-based headline classification approach to other languages, and how can they be addressed?

One of the primary challenges in applying the relevance-based headline classification approach to other languages is the availability of annotated data. Low-resource languages may lack sufficient annotated datasets for training classification models. To address this challenge, transfer learning techniques can be employed, where pre-trained models from high-resource languages are fine-tuned on a smaller annotated dataset in the target language. This approach helps in leveraging the knowledge captured by the pre-trained models and adapting it to the specific nuances of the target language. Additionally, language-specific features and characteristics need to be considered during the annotation process to ensure the relevance categories are accurately assigned. Collaborating with native speakers and domain experts can help in overcoming language-specific challenges and ensuring the quality of the annotated data.

How can the insights from this work be leveraged to improve headline generation in other domains, such as social media or user-generated content?

The insights from the relevance-based headline classification approach can be instrumental in improving headline generation in various domains, including social media and user-generated content. By training headline generation models on highly relevant article-headline pairs, the generated headlines are more likely to capture the essence of the content accurately. This can lead to more engaging and informative headlines, enhancing user experience and increasing reader engagement. Additionally, the classification of headlines based on relevance can help in filtering out clickbait or misleading headlines, ensuring that the generated content aligns with the actual information in the articles. By incorporating relevance-based classification techniques into headline generation models for social media and user-generated content, the quality and authenticity of headlines can be significantly improved, leading to more meaningful and impactful content delivery.