 As a result , the models may miss essential points conveyed in a document. Moreover, most of the existing methods are either extractive (identify important phrases from the document) or generative (generate phrases word by word), and hence they do not benefit from the advantages of both modeling techniques. To address these challenges , we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document , and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences.
<clarity>  As a result , the models may miss essential points conveyed in a document. Moreover, most of the existing methods are either extractive (identify important phrases from the document) or generative (generate phrases word by word), and hence they do not benefit from the advantages of both modeling techniques. To address these challenges , we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document , and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences.
 As a result , the models may miss essential points conveyed in the target document. To overcome this limitation , we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document , and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences.