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Pointer generator networks have been used successfully for abstractivesummarization. Along with the capability to generate novel words, it alsoallows the model to copy from the input text to handle out-of-vocabulary words.In this paper, we point out two key shortcomings of the summaries generatedwith this framework via manual inspection, statistical analysis and humanevaluation. The first shortcoming is the extractive nature of the generatedsummaries, since the network eventually learns to copy from the input articlemost of the times, affecting the abstractive nature of the generated summaries.The second shortcoming is the factual inaccuracies in the generated textdespite grammatical correctness. Our analysis indicates that this arises due toincorrect attention transition between different parts of the article. Wepropose an initial attempt towards addressing both these shortcomings byexternally appending traditional linguistic information parsed from the inputtext, thereby teaching networks on the structure of the underlying text.Results indicate feasibility and potential of such additional cues for improvedgeneration.

Improving generation quality of pointer networks via guided attention