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If there are multiple references, the ROUGE-1 scores are averaged. Because ROUGE is based only on content overlap, it can determine if the same general concepts are discussed between an automatic summary and a reference summary, but it cannot determine if the result is coherent or the sentences flow together in a sensible manner. High-order n-gram ROUGE measures try to judge fluency to some degree. Note that ROUGE is similar to the BLEU measure for machine translation, but BLEU is precision- based, because translation systems favor accuracy.
A promising approach is adaptive document/text summarization.<ref>{{Cite journal | doi=10.3103/S0005105510030027|title = Automatic genre recognition and adaptive text summarization| journal=Automatic Documentation and Mathematical Linguistics| volume=44| issue=3| pages=111–120|year = 2010|last1 = Yatsko|first1 = V. A.| last2=Starikov| first2=M. S.| last3=Butakov| first3=A. V.|s2cid = 1586931}}</ref> It involves first recognizing the text genre and then applying summarization algorithms optimized for this genre. Such software has been created.<ref>[http://yatsko.zohosites.com/universal-summarizer-unis.html UNIS (Universal Summarizer)]</ref>▼
====Supervised learning approaches====
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====Maximum entropy-based summarization====
During the DUC 2001 and 2002 evaluation workshops, [[Netherlands Organisation for Applied Scientific Research|TNO]] developed a sentence extraction system for multi-document summarization in the news ___domain. The system was based on a hybrid system using a [[naive Bayes]] classifier and statistical language models for modeling salience. Although the system exhibited good results, the researchers wanted to explore the effectiveness of a [[maximum entropy classifier|maximum entropy]] (ME) classifier for the meeting summarization task, as ME is known to be robust against feature dependencies. Maximum entropy has also been applied successfully for summarization in the broadcast news ___domain.
==== Adaptive summarization ====
▲A promising approach is adaptive document/text summarization.<ref>{{Cite journal |last1=Yatsko
====TextRank and LexRank====
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