# Automatic Generation of Factual News Headlines in Finnish

arXiv: https://arxiv.org/abs/2212.02170 · HTML: https://mlsanity.com/paper/2212.02170/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016)
- [A Theoretical Analysis of the Repetition Problem in Text Generation](https://mlsanity.com/paper/2012.14660/) (2020)
- [Automatic Text Summarization of COVID-19 Medical Research Articles using BERT and GPT-2](https://mlsanity.com/paper/2006.01997/) (2020)
- [Improving Truthfulness of Headline Generation](https://mlsanity.com/paper/2005.00882/) (2020)
- [Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers](https://mlsanity.com/paper/2108.00308/) (2021)
- [Controlling the Amount of Verbatim Copying in Abstractive Summarization](https://mlsanity.com/paper/1911.10390/) (2019)
- [Advances of Transformer-Based Models for News Headline Generation](https://mlsanity.com/paper/2007.05044/) (2020)
- [Template-free Data-to-Text Generation of Finnish Sports News](https://mlsanity.com/paper/1910.01863/) (2019)
- [The Great Misalignment Problem in Human Evaluation of NLP Methods](https://mlsanity.com/paper/2104.05361/) (2021)
- [Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline](https://mlsanity.com/paper/2106.03389/) (2021)
- [Low-Resource Neural Headline Generation](https://mlsanity.com/paper/1707.09769/) (2017)

## Built on by (most cited first, 1 of 1)

- [Emotion Conditioned Creative Dialog Generation](https://mlsanity.com/paper/2212.02907/) (2022)

## Abstract (arXiv)

We present a novel approach to generating news headlines in Finnish for a given news story. We model this as a summarization task where a model is given a news article, and its task is to produce a concise headline describing the main topic of the article. Because there are no openly available GPT-2 models for Finnish, we will first build such a model using several corpora. The model is then fine-tuned for the headline generation task using a massive news corpus. The system is evaluated by 3 expert journalists working in a Finnish media house. The results showcase the usability of the presented approach as a headline suggestion tool to facilitate the news production process.