  Extractive models usually formulate text summarization as extracting fixed
top-$k$ salient sentences from the document as a summary. Few works exploited
extracting finer-grained Elementary Discourse Unit (EDU) with little analysis
and justification for the extractive unit selection. Further, the selection
strategy of the fixed top-$k$ salient sentences fits the summarization need
poorly, as the number of salient sentences in different documents varies and
therefore a common or best $k$ does not exist in reality. To fill these gaps,
this paper first conducts the comparison analysis of oracle summaries based on
EDUs and sentences, which provides evidence from both theoretical and
experimental perspectives to justify and quantify that EDUs make summaries with
higher automatic evaluation scores than sentences. Then, considering this merit
of EDUs, this paper further proposes an EDU-level extractive model with Varying
summary Lengths and develops the corresponding learning algorithm. EDU-VL
learns to encode and predict probabilities of EDUs in the document, generate
multiple candidate summaries with varying lengths based on various $k$ values,
and encode and score candidate summaries, in an end-to-end training manner.
Finally, EDU-VL is experimented on single and multi-document benchmark datasets
and shows improved performances on ROUGE scores in comparison with
state-of-the-art extractive models, and further human evaluation suggests that
EDU-constituent summaries maintain good grammaticality and readability.
