Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an 'AI winter'), followed by new approaches, success and renewed funding. Capabilities generally classified as AI as of 2017 include successfully understanding human speech, competing at a high level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data, including images and videos. The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring 'intelligence' are often removed from the definition, a phenomenon known as the AI effect, leading to the quip 'AI is whatever hasn't been done yet.' For instance, optical character recognition is frequently excluded from 'artificial intelligence', having become a routine technology. Colloquially, the term 'artificial intelligence' is applied when a machine mimics 'cognitive' functions that humans associate with other human minds, such as 'learning' and 'problem solving'. In computer science AI research is defined as the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Rouge N, ehere N is n-gram reference_text = '''Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. Overlap of N-grams between the system and references summaries. That is how much the words (and/or n-grams) in the machine generates summaries appeared in the machine generated summaries. W(Human Reference Summary) In w(machine generates summary) Recall Oriented Understudy for Gisting Evaluation The closer a machine translation is to a professional human translation, the better it is.That is how much the word (and/or n-grams) in the machine generated summaries appeared in the human reference summaries.W(machine generates summary) in (Human reference Summary).Originally for machine translation(Bilingual).In Rouge we divide by the length of the human references, so we would need an additional penalty for longer system results which could artificially raise their Rouge score.įinally, you could use the F1 measure to make the metrics work together: F1 = 2 * (Bleu * Rouge) / (Bleu + Rouge) You could also implement something similar for Rouge, but this time penalizing system results which are longer than the general reference length, which would otherwise enable them to obtain artificially higher Rouge scores (since the longer the result, the higher the chance you would hit some word appearing in the references). This complements the n-gram metric behavior which in effect penalizes longer than reference results, since the denominator grows the longer the system result is. It penalizes system results which are shorter than the general length of a reference (read more about it here). There's something called brevity penalty, which is quite important and has already been added to standard Bleu implementations. If you have many words/ngrams from the system results appearing in the human references you will have high Bleu, and if you have many words/ngrams from the human references appearing in the system results you will have high Rouge. Naturally - these results are complementing, as is often the case in precision vs recall. Rouge measures recall: how much the words (and/or n-grams) in the human reference summaries appeared in the machine generated summaries. Bleu measures precision: how much the words (and/or n-grams) in the machine generated summaries appeared in the human reference summaries.

Summarize tool in microsoft word