  Pointer generator networks have been used successfully for abstractive
summarization. Along with the capability to generate novel words, it also
allows 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 generated
with this framework via manual inspection, statistical analysis and human
evaluation. The first shortcoming is the extractive nature of the generated
summaries, since the network eventually learns to copy from the input article
most of the times, affecting the abstractive nature of the generated summaries.
The second shortcoming is the factual inaccuracies in the generated text
despite grammatical correctness. Our analysis indicates that this arises due to
incorrect attention transition between different parts of the article. We
propose an initial attempt towards addressing both these shortcomings by
externally appending traditional linguistic information parsed from the input
text, thereby teaching networks on the structure of the underlying text.
Results indicate feasibility and potential of such additional cues for improved
generation.
