# Text2Time: Transformer-based Article Time Period Prediction

arXiv: https://arxiv.org/abs/2304.10859 · HTML: https://mlsanity.com/paper/2304.10859/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generating News Headlines with Recurrent Neural Networks](https://mlsanity.com/paper/1512.01712/) (2015)
- [Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP](https://mlsanity.com/paper/2111.01322/) (2021)
- [Unsupervised Pre-training for Biomedical Question Answering](https://mlsanity.com/paper/2009.12952/) (2020)
- [DTATG: An Automatic Title Generator based on Dependency Trees](https://mlsanity.com/paper/1710.00286/) (2017)

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

- [Temporal Preference Optimization for Unsupervised Retrieval](https://mlsanity.com/paper/2606.17664/) (2026)

## Abstract (arXiv)

The task of predicting the publication period of text documents, such as news articles, is an important but less studied problem in the field of natural language processing. Predicting the year of a news article can be useful in various contexts, such as historical research, sentiment analysis, and media monitoring. In this work, we investigate the problem of predicting the publication period of a text document, specifically a news article, based on its textual content. In order to do so, we created our own extensive labeled dataset of over 350,000 news articles published by The New York Times over six decades. In our approach, we use a pretrained BERT model fine-tuned for the task of text classification, specifically for time period prediction.This model exceeds our expectations and provides some very impressive results in terms of accurately classifying news articles into their respective publication decades. The results beat the performance of the baseline model for this relatively unexplored task of time prediction from text.