How to figure out whether two texts refer to the same object or event
This is a document clusterization problem. A general solution is to define some sort of distance between documents and apply a standard clusterization algorithm, such as k-means or expectation maximization. There are different approaches to picking distance measure $L$:


Build a common vocabulary and convert each document into a bag of words, i.e. a long vector consisting of zeros and ones of length $|Vocab|$. Then let $L$ be the Hamming distance between these two representations.
Compute word2vec embeddings of each word in a document and average them (can be also weighted by TF-IDF -- see this question on SO for details). This will produce a dense $d$-dimensional vector per each document. The distance $L$ can then be defined as Euclidean, Minkowski or more sophisticated distance on vectors in $\mathbb{R}^d$.
Compute doc2vec embedding directly. Once again it's a dense $d$-dimensional vector per document, so the same choice of vector distances applies.