- Abstract: Extractive summarization methods operate by ranking and selecting the sentences which best encapsulate the theme of a given document. They do not fare well in domains like fictional narratives where there is no central theme and core information is not encapsulated by a small set of sentences. For the purpose of reducing the size of the document while conveying the idea expressed by each sentence, we need more sentence specific methods. Telegraphic summarization, which selects short segments across several sentences, is better suited for such domains. Telegraphic summarization captures the plot better by retaining shorter versions of each sentence while not really concerning itself with grammatically linking these segments. In this paper, we propose an unsupervised deep learning network (NUTS) to generate telegraphic summaries. We use multiple encoder-decoder networks and learn to drop portions of the text that are inferable from the chosen segments. The model is agnostic to both sentence length and style. We demonstrate that the summaries produced by our model show significant quantitative and qualitative improvement over those produced by existing methods and baselines.
- Keywords: nlp, summarization, unsupervised learning, deep learning
- TL;DR: In this paper, we propose an unsupervised deep learning network (NUTS) to generate telegraphic summaries.