AI in Manufacturing: 4 Real-World Examples
Explore 4 real-world AI in manufacturing examples. From defect detection to predictive maintenance.
Explore 4 real-world AI in manufacturing examples. From defect detection to predictive maintenance.
This paper presents a new unsupervised approach to generating ultra-concise summaries of opinions. We formulate the problem of generating such a micropinion summary as an optimization problem, where we seek a set of concise and non-redundant phrases that are readable and represent key opinions in text. We measure representativeness based on a modified mutual information function and model readability with an n-gram language model.
The Opinosis Summarization framework focuses on generating very short abstractive summaries from large amounts of text. These summaries can resemble micropinions or “micro-reviews” that you see on sites like twitter and four squares. The idea of the algorithm is to use a word graph data structure referred to as the Opinosis-Graph to represent the text to be summarized. Then, the resulting graph is repeatedly explored to find meaningful paths which in turn becomes candidate summary phrases.
While much work has been done in the area of extractive summarization, there has been limited study in abstractive summarization as this is much harder to achieve (going by the definition of true abstraction). This page contains a very small collection of summarization methods that are non-extractive…
These are some handy notes for MEAD. What is MEAD? MEAD is a publicly available framework for summarization. It is not really an ‘algorithm’. By default (I guess when it was first implemented) it was developed based on a centroid based approach…
This article talks about how to work with ROUGE for evaluation of summarization tasks. The original ROUGE toolkit uses a perl implementation which is really hard to understand, so I decided to piece together some information that may be helpful to others…