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:
Document coherence describes how much sense text makes in terms of its logical organisation and discourse flow. Even though coherence is a relatively difficult notion to quantify precisely, it can be approximated automatically. This type of coherence modelling is not only interesting in itself, but also useful for a number of other text processing tasks, including Information Retrieval (IR), where adjusting the ranking of documents according to both their relevance and their coherence has been shown to increase retrieval effectiveness [34,37]. The state of the art in unsupervised coherence modelling represents documents as bipartite graphs of sentences and discourse entities, and then projects these bipartite graphs into one-mode undirected graphs. However, one-mode projections may incur significant loss of the information present in the original bipartite structure. To address this we present three novel graph metrics that compute document coherence on the original bipartite graph of sentences and entities. Evaluation on standard settings shows that: (i) one of our coherence metrics beats the state of the art in terms of coherence accuracy; and (ii) all three of our coherence metrics improve retrieval effectiveness because, as closer analysis reveals, they capture aspects of document quality that go undetected by both keyword-based standard ranking and by spam filtering. This work contributes document coherence metrics that are theoretically principled, parameter-free, and useful to IR.

Cited Abstract(s):
[1]: Coherence is what makes a multi-sentence text meaningful, both logically and syntactically. To solve the challenge of ordering a set of sentences into coherent order, existing approaches focus mostly on defining and using sophisticated features to capture the cross-sentence argumentation logic and syntactic relationships. But both argumentation semantics and crosssentence syntax (such as coreference and tense rules) are very hard to formalize. In this paper, we introduce a neural network model for the coherence task based on distributed sentence representation. The proposed approach learns a syntacticosemantic representation for sentences automatically, using either recurrent or recursive neural networks. The architecture obviated the need for feature engineering, and learns sentence representations, which are to some extent able to capture the ‘rules’ governing coherent sentence structure. The proposed approach outperforms existing baselines and generates the stateof-art performance in standard coherence evaluation tasks 1 .
To our knowledge, the current state of the art in coherence modelling in terms of accuracy is the deep learning approach of [1] , where a recursive neural network learns sentential compositionality and is then used to model document coherence. This approach is supervised and computationally much heavier than graph-based coherence modelling.