# Advances of Transformer-Based Models for News Headline Generation

arXiv: https://arxiv.org/abs/2007.05044 · HTML: https://mlsanity.com/paper/2007.05044/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 10 of 10)

- ["Why is this misleading?": Detecting News Headline Hallucinations with Explanations](https://mlsanity.com/paper/2302.05852/) (2023)
- [Fine-tuning GPT-3 for Russian Text Summarization](https://mlsanity.com/paper/2108.03502/) (2021)
- [SciNews: From Scholarly Complexities to Public Narratives -- A Dataset for Scientific News Report Generation](https://mlsanity.com/paper/2403.17768/) (2024)
- [Tweetorial Hooks: Generative AI Tools to Motivate Science on Social Media](https://mlsanity.com/paper/2305.12265/) (2023)
- [XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags](https://mlsanity.com/paper/2406.03776/) (2024)
- [Automatic Generation of Factual News Headlines in Finnish](https://mlsanity.com/paper/2212.02170/) (2022)
- [BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusion](https://mlsanity.com/paper/2501.01069/) (2025)
- [Cross-Domain Robustness of Transformer-based Keyphrase Generation](https://mlsanity.com/paper/2312.10700/) (2023)
- [Improving Sequence-to-Sequence Models for Abstractive Text Summarization Using Meta Heuristic Approaches](https://mlsanity.com/paper/2403.16247/) (2024)
- [TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu](https://mlsanity.com/paper/2404.11349/) (2024)

## Abstract (arXiv)

Pretrained language models based on Transformer architecture are the reason for recent breakthroughs in many areas of NLP, including sentiment analysis, question answering, named entity recognition. Headline generation is a special kind of text summarization task. Models need to have strong natural language understanding that goes beyond the meaning of individual words and sentences and an ability to distinguish essential information to succeed in it. In this paper, we fine-tune two pretrained Transformer-based models (mBART and BertSumAbs) for that task and achieve new state-of-the-art results on the RIA and Lenta datasets of Russian news. BertSumAbs increases ROUGE on average by 2.9 and 2.0 points respectively over previous best score achieved by Phrase-Based Attentional Transformer and CopyNet.