Text Summarization Techniques: A Brief Survey <s> Scientific Articles Summarization <s> In this paper, we present a study of a novel summarization problem, i.e., summarizing the impact of a scientic publication. Given a paper and its citation context, we study how to extract sentences that can represent the most inuential content of the paper. We propose language modeling methods for solving this problem, and study how to incorporate features such as authority and proximity to accurately estimate the impact language model. Experiment results on a SIGIR publication collection show that the proposed methods are effective for generating impact-based summaries. <s> BIB001 </s> Text Summarization Techniques: A Brief Survey <s> Scientific Articles Summarization <s> Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others' viewpoint of the target article's contributions and the study of its citation summary network using a clustering approach. <s> BIB002 </s> Text Summarization Techniques: A Brief Survey <s> Scientific Articles Summarization <s> In citation-based summarization, text written by several researchers is leveraged to identify the important aspects of a target paper. Previous work on this problem focused almost exclusively on its extraction aspect (i.e. selecting a representative set of citation sentences that highlight the contribution of the target paper). Meanwhile, the fluency of the produced summaries has been mostly ignored. For example, diversity, readability, cohesion, and ordering of the sentences included in the summary have not been thoroughly considered. This resulted in noisy and confusing summaries. In this work, we present an approach for producing readable and cohesive citation-based summaries. Our experiments show that the proposed approach outperforms several baselines in terms of both extraction quality and fluency. <s> BIB003
A useful source of information when summarizing a scientific paper (i.e. citation-based summarization) is to find other papers that cite the target paper and extract the sentences in which the references take place in order to identify the important aspects of the target paper. Mei et al. BIB001 propose a language model that gives a probability to each word in the citation context sentences. They then score the importance of sentences in the original paper using the KL divergence method (i.e. finding the similarity between a sentence and the language model). For more information, see BIB003 BIB002 