  Word frequency-based methods for extractive summarization are easy to
implement and yield reasonable results across languages. However, they have
significant limitations - they ignore the role of context, they offer uneven
coverage of topics in a document, and sometimes are disjointed and hard to
read. We use a simple premise from linguistic typology - that English sentences
are complete descriptors of potential interactions between entities, usually in
the order subject-verb-object - to address a subset of these difficulties. We
have developed a hybrid model of extractive summarization that combines
word-frequency based keyword identification with information from automatically
generated entity relationship graphs to select sentences for summaries.
Comparative evaluation with word-frequency and topic word-based methods shows
that the proposed method is competitive by conventional ROUGE standards, and
yields moderately more informative summaries on average, as assessed by a large
panel (N=94) of human raters.
