<p>Obviously, this is a huge and busy research area, but I'd say there are two broad types of approaches you could look into:</p>

<p>First, there are some methods that learn sentence embeddings in an unsupervised manner, such as <a href="https://arxiv.org/pdf/1405.4053v2.pdf" rel="nofollow noreferrer">Le and Mikolov's (2014) Paragraph Vectors</a>, which are implemented in <a href="https://radimrehurek.com/gensim/models/doc2vec.html" rel="nofollow noreferrer">gensim</a>, or <a href="https://arxiv.org/abs/1506.06726" rel="nofollow noreferrer">Kiros et al.'s (2015) SkipThought vectors</a>, with an <a href="https://github.com/ryankiros/skip-thoughts" rel="nofollow noreferrer">implementation on Github</a>.</p>

<p>Then there also exist supervised methods that learn sentence embeddings from labelled data. The most recent one is <a href="https://arxiv.org/abs/1705.02364" rel="nofollow noreferrer">Conneau et al.'s (2017)</a>, which trains sentence embeddings on the Stanford Natural Language Inference dataset, and shows these embeddings can be used successfully across a range of NLP tasks. The code is <a href="https://github.com/facebookresearch/InferSent" rel="nofollow noreferrer">available on Github</a>.</p>

<p>You might also find some inspiration in <a href="http://nlp.yvespeirsman.be/blog/anything2vec/" rel="nofollow noreferrer">a blog post I wrote earlier this year on the topic of embeddings</a>.</p>
Recent methods for finding semantic similarity between two short sentences or articles (on a concept level)
<p>I'm working on finding similarities between short sentences and articles. I used many existing methods such as tf-idf, word2vec etc but the results are just okay. The most relevant measure which I found was word moving distance, however, its results are not that better than the other measures. I know it's a challenging problem, however, I am wondering if there are any new methods to find an approximate similarity more on a higher or concept level than just matching words.  Especially, any alternative new methods like word moving distance which looks at slightly higher semantic of a sentence or article?</p>