What other approaches are there for abstractive summarization, other then seq2seq? I'm researching on abstractive text summarization, and has come across many recent papers. They all seem to be focusing on Sequence to Sequence models based on RNNs. Apart from RNNs, what other approaches are there when it comes to abstractive text summarization? Does ontology-based summarization revolve around the same seq2seq model?
Most of the material I have come across are research papers on this subject; what better sources are there to get an understanding of the underlining concepts of abstractive summarization?
 A: 
Apart from RNNs, what other approaches are there when it comes to abstractive text summarization? 

There are many other systems, I copy below some references to them:
From {1}:

Unfortunately, abstractive summarization is
  known to be difficult. Existing work in abstractive
  summarization has been quite limited and can be
  categorized into two categories: (1) approaches using
  prior knowledge (Radev and McKeown, 1998)
  (Finley and Harabagiu, 2002) (DeJong, 1982) and
  (2) approaches using Natural Language Generation
  (NLG) systems (Saggion and Lapalme, 2002)
  (Jing and McKeown, 2000). The first line of work
  requires considerable amount of manual effort to
  define schemas such as frames and templates that
  can be filled with the use of information extraction
  techniques. These systems were mainly used to
  summarize news articles. The second category of
  work uses deeper NLP analysis with special techniques
  for text regeneration. Both approaches either
  heavily rely on manual effort or are domain
  dependent.

You may also want to have at section 2.3.2 Toward full abstraction and table 1 from {2}:


2.3.2 Toward full abstraction
Fully abstractive summarization attempts to understand the input and generate the summary
  from scratch, usually including sentences or phrases that may not appear in the original document.
  It actually involves multiple subproblems, each of its own can be made a relatively
  independent research topic, including: simplification, paraphrasing, merging or fusion. Cheung
  and Penn [29] conduct a series of studies comparing human-written model summaries to
  system summaries at the semantic level of caseframes, which are shallow approximations of
  the semantic role structure of a proposition-bearing unit like a verb and are derived from the
  dependency parse of a sentence. They find that human summaries are: (1) more abstractive,
  using more aggregation, (2) contain less caseframes and (3) cannot be reconstructed solely
  from original documents but are able to if in-domain documents are added.
Due to the inherent difficulty and complexity of full abstraction, current research in abstractive
  document summarizationmostly restricts in one or a fewof the subproblems. It is also less
  active compared with compressive summarization, since merely considering compressions
  has already boosted system performance, as discussed in the last section.9
Woodsend and Lapata [195] propose a model that allows paraphrases induced from a
  quasi-synchronous tree substitution grammar (QTSG) to be selected in the final ILP model
  covering content selection, surface realization, paraphrasing and stylistic conventions. For
  document summarization that involves paraphrasing and fusing multiple sentences simultaneously,
  other than grammar-based rewriting, one simpler more typical approach is to merge
  information contained in sub-sentence-level units. For instance, one can cluster sentences,
  build word graphs and generate (shortest) paths from each cluster to produce candidates for
  making up a summary [6,51]. More sophisticated treatments can also be built on syntactic or
  semantic analysis. One may build sentences via merging consistent noun phrases and verb
  phrases [13] or linearizing graph-based semantic units derived from semantic formalisms
  such as abstract meaning representation (AMR) [121].
There also exist psychologically motivated studies [48] trying to implement cognitive
  human comprehension models based on propositions, which are elements extracted from an
  original sentence, each containing one functor and several arguments. Propositions form a
  tree where a proposition is attached to another proposition with which they share at least one
  argument. Summaries are then generated from selected important propositions. Currently
  the systems have mostly been evaluated on over-specific datasets and rely heavily on various
  components including parsing, coreference resolution, distributional semantics, lexical
  chains [49] and natural language generation from semantic graphs [50
In order to better guide alignment and merging processes, supervised learning-basedmethods
  have been investigated [46,178]. A later work [30] expands the sentence fusion process
  with external resources beyond the input sentences by combining the subtrees of many sentences,
  allowing for relevant information from sentences that are not similar to the original
  input sentences to be added during fusion.
Abstractive summarization has also been studied in information extraction (IE) perspective,
  for example, IE-informed metrics have also been shown to be useful to rerank the output
  of high performing baseline summarization systems [83]. In the context of guided summarization
  where predefined categories and readers’ intent have been predefined, preliminary
  full abstraction can be achieved by extracting templates using predefined rules for different
  types of events [59,166].
In order to better guide alignment and merging processes, supervised learning-basedmethods
  have been investigated [46,178]. A later work [30] expands the sentence fusion process
  with external resources beyond the input sentences by combining the subtrees of many sentences,
  allowing for relevant information from sentences that are not similar to the original
  input sentences to be added during fusion.
  Abstractive summarization has also been studied in information extraction (IE) perspective,
  for example, IE-informed metrics have also been shown to be useful to rerank the output
  of high performing baseline summarization systems [83]. In the context of guided summarization
  where predefined categories and readers’ intent have been predefined, preliminary
  full abstraction can be achieved by extracting templates using predefined rules for different
  types of events [59,166].


References:


*

*{1} Ganesan, Kavita, ChengXiang Zhai, and Jiawei Han. "Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions." In Proceedings of the 23rd international conference on computational linguistics, pp. 340-348. Association for Computational Linguistics, 2010.
Harvard ; http://www.anthology.aclweb.org/C/C10/C10-1039.pdf ; https://scholar.google.com/scholar?cluster=17683373799863050123&hl=en&as_sdt=0,5

*{2} Yao, Jin-ge, Xiaojun Wan, and Jianguo Xiao. "Recent advances in document summarization." Knowledge and Information Systems (2017): 1-40. https://scholar.google.com/scholar?cluster=16155547405797487545&hl=en&as_sdt=0,5
