Second, we compare different strategies to utilize a pre-trained seq2seq model to generate and select a set of questions related to a given paragraph.
Paraphrase generation is a fundamental and long-standing task in natural language processing.
We discover that adding sentences from the full text particularly in the form of summary of the article can significantly improve the generation of both types of keyphrases that are either present or absent from the title and abstract.
Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document.
We also show that CRvNN performs comparably or better than prior latent structure models on real-world tasks such as sentiment analysis and natural language inference.
Distinguishing informative and actionable messages from a social media platform like Twitter is critical for facilitating disaster management.
Previously, joint training of two different layers of a stacked Recurrent Neural Network for keyword discovery and keyphrase extraction had been shown to be effective in extracting keyphrases from general Twitter data.