# LSTM-Based Text Generation: A Study on Historical Datasets

arXiv: https://arxiv.org/abs/2403.07087 · HTML: https://mlsanity.com/paper/2403.07087/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Story Generation from Sequence of Independent Short Descriptions](https://mlsanity.com/paper/1707.05501/) (2017)
- [Deep Gate Recurrent Neural Network](https://mlsanity.com/paper/1604.02910/) (2016)

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

- [First Place Solution of 2023 Global Artificial Intelligence Technology Innovation Competition Track 1](https://mlsanity.com/paper/2407.01271/) (2024)

## Abstract (arXiv)

This paper presents an exploration of Long Short-Term Memory (LSTM) networks in the realm of text generation, focusing on the utilization of historical datasets for Shakespeare and Nietzsche. LSTMs, known for their effectiveness in handling sequential data, are applied here to model complex language patterns and structures inherent in historical texts. The study demonstrates that LSTM-based models, when trained on historical datasets, can not only generate text that is linguistically rich and contextually relevant but also provide insights into the evolution of language patterns over time. The finding presents models that are highly accurate and efficient in predicting text from works of Nietzsche, with low loss values and a training time of 100 iterations. The accuracy of the model is 0.9521, indicating high accuracy. The loss of the model is 0.2518, indicating its effectiveness. The accuracy of the model in predicting text from the work of Shakespeare is 0.9125, indicating a low error rate. The training time of the model is 100, mirroring the efficiency of the Nietzsche dataset. This efficiency demonstrates the effectiveness of the model design and training methodology, especially when handling complex literary texts. This research contributes to the field of natural language processing by showcasing the versatility of LSTM networks in text generation and offering a pathway for future explorations in historical linguistics and beyond.