Auto-encoders compress input data into a latent-space representation and reconstruct the original data from the representation. This latent representation is not easily interpreted by humans. In this paper, we propose training an auto-encoder that encodes input text into human-readable sentences. The auto-encoder is composed of a generator and a reconstructor. The generator encodes the input text into a shorter word sequence, and the reconstructor recovers the generator input from the generator output. To make the generator output human-readable, a discriminator restricts the output of the generator to resemble human-written sentences. By taking the generator output as the summary of the input text, abstractive summarization is achieved without document-summary pairs as training data. Promising results are shown on both English and Chinese corpora.
Summary: In this work, the authors propose a text reconstructing auto encoder which takes a sentence as the input sequence and an integrated text generator generates another version of the input text while a reconstructor determines how well this generated text reconstructs the original input sequence. The input to the discriminator (as real data) is a sentence that summarizes the ground truth sentences (rather than the ground truth sentences themselves). The experiments are conducted in two datasets of English and Chinese corpora.
Strengths:
The proposed idea of generating text using summary sentences is new.
The model overview in Figure 1 is informative.
The experiments are conducted on English and Chinese corpora, comparison with competitive baselines are provided.
Weaknesses:
The paper is poorly written which makes it difficult to understand. The second paragraph in the introduction is quite cryptic. Even after reading the entire paper a couple of times, it is not clear how the summary text is obtained, e.g. do the authors ask annotators to read sentences and summarize them? If so, based on which criteria do the annotators summarize text, how many annotators are there? Similarly, if so this would mean that the authors use additional supervision than the compared models. Please clarify how the summary text is obtained.
In footnote 1, the authors mention “seq2seq2seq2” term which they do not explain anywhere in the text.
No experiments that generate raw text (without using summaries) are provided. It would be interesting to see if GAN learns to memorize the ground truth sentences or generates sentences with enough variation. 
In the English Gigaword dataset the results consistently drop compared to WGAN. This behavior is observed for both the unsupervised setting and two versions of transfer learning settings. There are too few qualitative results: One positive qualitative result is provided in Figure 3 and one negative qualitative result is provided in Figure 4. Therefore, it is not easy for the reader to judge the behavior of the model well. 
The choice of the evaluation metric is not well motivated. The standard measures in the literature also include METEOR, CIDER and SPICE. It would be interesting to see how the proposed model performs in these additional criteria. Moreover, the results are not sufficiently discussed. 
As a general remark, although the idea presented in this paper is interesting, both in terms of writing and evaluation, this paper has not yet reached the maturity expected from an ICLR paper. Regarding writing, the definite and indefinite articles are sometimes missing and sometimes overused, similarly most of the times there is a singular/plural mismatch. This makes the paper very difficult to read. Often the reader needs to guess what is actually meant. Regarding the experiments, presenting results with multiple evaluation criteria and showing more qualitative results would improve the exposition.
Minor comments:
Page 5: real or false —> real or fake (true or false)
	     the lower loss it get —> ?