  Many scientific papers such as those in arXiv and PubMed data collections
have abstracts with varying lengths of 50-1000 words and average length of
approximately 200 words, where longer abstracts typically convey more
information about the source paper. Up to recently, scientific summarization
research has typically focused on generating short, abstract-like summaries
following the existing datasets used for scientific summarization. In domains
where the source text is relatively long-form, such as in scientific documents,
such summary is not able to go beyond the general and coarse overview and
provide salient information from the source document. The recent interest to
tackle this problem motivated curation of scientific datasets, arXiv-Long and
PubMed-Long, containing human-written summaries of 400-600 words, hence,
providing a venue for research in generating long/extended summaries. Extended
summaries facilitate a faster read while providing details beyond coarse
information. In this paper, we propose TSTR, an extractive summarizer that
utilizes the introductory information of documents as pointers to their salient
information. The evaluations on two existing large-scale extended summarization
datasets indicate statistically significant improvement in terms of Rouge and
average Rouge (F1) scores (except in one case) as compared to strong baselines
and state-of-the-art. Comprehensive human evaluations favor our generated
extended summaries in terms of cohesion and completeness.
