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:
Keyword and keyphrase extraction is an important problem in natural language processing, with applications ranging from summarization to semantic search to document clustering. Graph-based approaches to keyword and keyphrase extraction avoid the problem of acquiring a large in-domain training corpus by applying variants of PageRank algorithm on a network of words. Although graph-based approaches are knowledge-lean and easily adoptable in online systems, it remains largely open whether they can benefit from centrality measures other than PageRank. In this paper, we experiment with an array of centrality measures on word and noun phrase collocation networks, and analyze their performance on four benchmark datasets. Not only are there centrality measures that perform as well as or better than PageRank, but they are much simpler (e.g., degree, strength, and neighborhood size). Furthermore, centrality-based methods give results that are competitive with and, in some cases, better than two strong unsupervised baselines.

Cited Abstract(s):
[1]: Keywords can be considered as condensed versions of documents and short forms of their summaries. In this paper, the problem of automatic extraction of keywords from documents is treated as a supervised learning task. A lexical chain holds a set of semantically related words of a text and it can be said that a lexical chain represents the semantic content of a portion of the text. Although lexical chains have been extensively used in text summarization, their usage for keyword extraction problem has not been fully investigated. In this paper, a keyword extraction technique that uses lexical chains is described, and encouraging results are obtained.

[2]: In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications.

[3]: In this paper, we study different centrality measures being used in predicting noun phrases appearing in the abstracts of scientific articles. Our experimental results show that centrality measures improve the accuracy of the prediction in terms of both precision and recall. We also found that the method of constructing Noun Phrase Network significantly influences the accuracy when using the centrality heuristics itself, but is negligible when it is used together with other text features in decision trees.

[4]: Existing methods for single document keyphrase extraction usually make use of only the information contained in the specified document. This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. A specified document is expanded to a small document set by adding a few neighbor documents close to the document, and the graph-based ranking algorithm is then applied on the expanded document set to make use of both the local information in the specified document and the global information in the neighbor documents. Experimental results demonstrate the good effectiveness and robustness of our proposed approach.
Graph-based methods to key term extraction are inherently unsupervised, where the philosophy is to build a network of words phrases, and then rank the nodes using some kind of centrality measure. Researchers used variants of PageRank [2] [4] , HITS [1] , and other measures like degree, betweenness and closeness [3] . Use of noun phrase networks has been reported in [3] .