Summary of Lstm-based Text Generation: a Study on Historical Datasets, by Mustafa Abbas Hussein Hussein et al.
LSTM-Based Text Generation: A Study on Historical Datasets
by Mustafa Abbas Hussein Hussein, Serkan Savaş
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of Long Short-Term Memory (LSTM) networks in text generation, specifically focusing on modeling complex language patterns and structures inherent in historical texts. By training LSTM-based models on datasets from Shakespeare and Nietzsche, researchers demonstrate that these models can generate linguistically rich and contextually relevant text, as well as provide insights into the evolution of language patterns over time. The study highlights the effectiveness of LSTMs in predicting text from works by both authors, with high accuracy (0.9521 for Nietzsche and 0.9125 for Shakespeare) and efficiency (100 iterations). This research contributes to natural language processing, showcasing the versatility of LSTM networks in text generation and offering a pathway for future explorations in historical linguistics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can learn to generate text that sounds like it was written by famous authors from history. The researchers used special computer models called Long Short-Term Memory (LSTM) networks to analyze texts from Shakespeare and Nietzsche. They found that these models could create new text that is very similar to the original writings, and even help us understand how language has changed over time. The study shows that these models can be very accurate and efficient in predicting what words or phrases might come next in a piece of writing. This research helps us better understand how computers can learn from historical texts and generate new text that is meaningful and relevant. |
Keywords
» Artificial intelligence » Lstm » Natural language processing » Text generation
