Extractive Summary as Discrete Latent Variables
This work addresses text compression and generation for natural language processing, offering incremental insights by validating classical methods like tf-idf in neural contexts.
The paper tackled text compression by comparing extractive methods using neural models and found that extracting tokens as latent variables outperforms state-of-the-art discrete latent variable models like VQ-VAE, with two best-performing methods achieving equal results: selecting tokens based on highest tf-idf scores or highest loss from a bidirectional language model.
In this paper, we compare various methods to compress a text using a neural model. We find that extracting tokens as latent variables significantly outperforms the state-of-the-art discrete latent variable models such as VQ-VAE. Furthermore, we compare various extractive compression schemes. There are two best-performing methods that perform equally. One method is to simply choose the tokens with the highest tf-idf scores. Another is to train a bidirectional language model similar to ELMo and choose the tokens with the highest loss. If we consider any subsequence of a text to be a text in a broader sense, we conclude that language is a strong compression code of itself. Our finding justifies the high quality of generation achieved with hierarchical method, as their latent variables are nothing but natural language summary. We also conclude that there is a hierarchy in language such that an entire text can be predicted much more easily based on a sequence of a small number of keywords, which can be easily found by classical methods as tf-idf. We speculate that this extraction process may be useful for unsupervised hierarchical text generation.