  Sentence position is a strong feature for news summarization, since the lead
often (but not always) summarizes the key points of the article. In this paper,
we show that recent neural systems excessively exploit this trend, which
although powerful for many inputs, is also detrimental when summarizing
documents where important content should be extracted from later parts of the
article. We propose two techniques to make systems sensitive to the importance
of content in different parts of the article. The first technique employs
'unbiased' data; i.e., randomly shuffled sentences of the source document, to
pretrain the model. The second technique uses an auxiliary ROUGE-based loss
that encourages the model to distribute importance scores throughout a document
by mimicking sentence-level ROUGE scores on the training data. We show that
these techniques significantly improve the performance of a competitive
reinforcement learning based extractive system, with the auxiliary loss being
more powerful than pretraining.
