  How to efficiently generate an accurate, well-structured overview report
(ORPT) over thousands of related documents is challenging. A well-structured
ORPT consists of sections of multiple levels (e.g., sections and subsections).
None of the existing multi-document summarization (MDS) algorithms is directed
toward this task. To overcome this obstacle, we present NDORGS (Numerous
Documents' Overview Report Generation Scheme) that integrates text filtering,
keyword scoring, single-document summarization (SDS), topic modeling, MDS, and
title generation to generate a coherent, well-structured ORPT. We then devise a
multi-criteria evaluation method using techniques of text mining and
multi-attribute decision making on a combination of human judgments, running
time, information coverage, and topic diversity. We evaluate ORPTs generated by
NDORGS on two large corpora of documents, where one is classified and the other
unclassified. We show that, using Saaty's pairwise comparison 9-point scale and
under TOPSIS, the ORPTs generated on SDS's with the length of 20% of the
original documents are the best overall on both datasets.
