English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work we present an unsupervised approach to summarize sentences inabstractive way using Variational Autoencoder (VAE). VAE are known to learn asemantically rich latent variable, representing high dimensional input. VAEsare trained by learning to reconstruct the input from the probabilistic latentvariable. Explicitly providing the information about output length duringtraining influences the VAE to not encode this information and thus can bemanipulated during inference. Instructing the decoder to produce a shorteroutput sequence leads to expressing the input sentence with fewer words. Weshow on different summarization data sets, that these shorter sentences can notbeat a simple baseline but yield higher ROUGE scores than trying to reconstructthe whole sentence.

Unsupervised Abstractive Sentence Summarization using Length Controlled Variational Autoencoder