You have got text which have words. Now each of the words have dependency with words that are occurring previously or/and with the ones occurring later. So to capture the "context" in which a particular word occurs is done by sequence models in deep learning. Sequence models that are quite used often are Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM) listed in the order of their functionality and complexity. To know more about them visit Jonathan Hui's blog.
Your problem is a classification problem i.e. given a sentence the classifier should tell which chapter and section it belongs to. You can use one of the sequence models to "encode" the sentence as a vector (based on the context of words in it and meaning of sentence) and pass that vector to a fully-connected neural network that has a softmax layer at the end to tell which chapter and section the text belongs to. The number of classes you are having are
C * S (one for each chapter-section pair). From the output of softmax layer you can pick up the top 3 classes that got the highest probability for a sentence.
Doc2Vec converts a paragraph (can also be a sentence) into a vector representation. The Doc2Vec model has been trained over a huge corpus of text to be able to capture the context of a text and represent it as a high dimensional vector.
The corpus of text that you have, is from a probability distribution is quite different from that used for training Doc2Vec model. Sequence models (mentioned above) can learn that probability distribution for your text corpus exclusively. In other words, you have a tailor-made representation for your text so that the context can be understood much better.
That is why I suggested you to use sequence models. You can also compare the performance obtained by using pretrained Doc2Vec as text representation with that obtained by using one of the sequence models.
To know about the effectiveness of RNN visit Andrej Karpathy's blog.