Conditional Independence and Marginalization

I'm currently reading Bishops: Pattern Matching and Machine Learning and ran into an equation I didn't grasp. Page 373, Graphical Models, section 8.2.1.

He says:

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Given the joint distribution:

$$p(a,b,c) = p(a|c)p(b|c)p(c)$$ We can investigate whether a and b are independent by marginalizing both sides with respect to c, $$p(a,b) = \sum_c p(a|c)p(b|c)p(c)$$ and in general this does NOT factorize to the product: $$p(a)p(b)$$

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My question is why it would not factor into the product above? Seems reasonable that once c has been marginalized out that's exactly the product you would get?

Summing over all values of c should result in: $$\sum_c p(c) = 1$$ and thus we should arrive at the product, $$p(a,b) = \sum_c p(a|c)p(b|c)=p(a)p(b)$$ What am I missing?

Note that, in general, $$\sum_c p(a|c) p(b|c) p(c) \ne p(a) p(b) \sum_c p(c)\,.$$
The fact that $\sum_c p(c) = 1$ can only be used if $p(a|c) = p(a)$ and $p(b|c) = p(b)$, which is not always true. In general $\sum_c p(c) = 1$ will not be useful. Consider for example, consider for $c \in \{1,2\}$
$$p(a|c) = ce^{-ca}, p(b|c) = ce^{-cb}, \text{ and } p(c) = 1/2$$
The above cannot be factored in $p(a)p(b)$.