You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We propose a deep learning model for identifying structure within experiment narratives in scientific literature. We take a sequence labeling approach to this problem, and label clauses within experiment narratives to identify the different parts of the experiment. Our dataset consists of paragraphs taken from open access PubMed papers labeled with rhetorical information as a result of our pilot annotation. Our model is a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) cells that labels clauses. The clause representations are computed by combining word representations using a novel attention mechanism that involves a separate RNN. We compare this model against LSTMs where the input layer has simple or no attention and a feature rich CRF model. Furthermore, we describe how our work could be useful for information extraction from scientific literature.

Cited Abstract(s):
[1]: The specification discloses a luggage carrier made up of a generally U-shaped frame. The frame has two spaced legs with a hook on the front which hooks over the bumper of an automobile. Two braces are attached to the cross member of the U-shaped member and the front portion of the braces is received on fastening means welded to the under side of the car frame. The cross members provide a supporting surface for carrying articles, boats and the like. A platform may be supported on the frame.

[2]: Natural language parsing has typically been done with small sets of discrete categories such as NP and VP, but this representation does not capture the full syntactic nor semantic richness of linguistic phrases, and attempts to improve on this by lexicalizing phrases or splitting categories only partly address the problem at the cost of huge feature spaces and sparseness. Instead, we introduce a Compositional Vector Grammar (CVG), which combines PCFGs with a syntactically untied recursive neural network that learns syntactico-semantic, compositional vector representations. The CVG improves the PCFG of the Stanford Parser by 3.8 to obtain an F1 score of 90.4 . It is fast to train and implemented approximately as an efficient reranker it is about 20 faster than the current Stanford factored parser. The CVG learns a soft notion of head words and improves performance on the types of ambiguities that require semantic information such as PP attachments.
General domain discourse parsing is a well-studied problem. While there are many discourse theories (see [2] , chapter 2 for an overview), Rhetorical Structure Theory (RST) by [1] , received a lot of attention. It is generally accepted that relations between non-overlapping chunks of text need to be considered to account for the overall meaning . Accordingly, rhetorical relations are central in RST for marking the structure. In contrast, the taxonomy we use applies to the clauses themselves, instead of the relations between them. This is made possible by the specificity of our domain: In the general case, it may not be possible to identify the type of a clause in isolation. However, it has to be noted that the information conveyed by our clause-centric formalism may also be expressed using a relation-centric discourse formalism like RST. Figure shows one possible RST tree for the text shown in Figure .