Summarizing novel chapters is a difficult task due to the input length and
the fact that sentences that appear in the desired summaries draw content from
multiple places throughout the chapter. We present a pipelined
extractive-abstractive approach where the extractive step filters the content
that is passed to the abstractive component. Extremely lengthy input also
results in a highly skewed dataset towards negative instances for extractive
summarization; we thus adopt a margin ranking loss for extraction to encourage
separation between positive and negative examples. Our extraction component
operates at the constituent level; our approach to this problem enriches the
text with spinal tree information which provides syntactic context (in the form
of constituents) to the extraction model. We show an improvement of 3.71
Rouge-1 points over best results reported in prior work on an existing novel
chapter dataset