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Found 2 papers, 2 papers with code
Text Length Adaptation in Sentiment Classification
We propose a state-of-the-art CLT model called Length Transfer Networks (LeTraNets) that introduces a two-way encoding scheme for short and long texts using multiple training mechanisms.
Entity Commonsense Representation for Neural Abstractive Summarization
To this end, we leverage on an off-the-shelf entity linking system (ELS) to extract linked entities and propose Entity2Topic (E2T), a module easily attachable to a sequence-to-sequence model that transforms a list of entities into a vector representation of the topic of the summary.
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Text Summarization
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