Abstract
Text summarization is a process of producing a concise version of text (summary) from one or more information sources. If the generated summary preserves meaning of the original text, it will help the users to make fast and effective decision. However, how much meaning of the source text can be preserved is becoming harder to evaluate. The most commonly used automatic evaluation metrics like Recall-Oriented Understudy for Gisting Evaluation (ROUGE) strictly rely on the overlapping n-gram units between reference and candidate summaries, which are not suitable to measure the quality of abstractive summaries. Another major challenge to evaluate text summarization systems is lack of consistent ideal reference summaries. Studies show that human summarizers can produce variable reference summaries of the same source that can significantly affect automatic evaluation metrics scores of summarization systems. Humans are biased to certain situation while producing summary, even the same person perhaps produces substantially different summaries of the same source at different time. This paper proposes a word embedding based automatic text summarization and evaluation framework, which can successfully determine salient top-n sentences of a source text as a reference summary, and evaluate the quality of systems summaries against it. Extensive experimental results demonstrate that the proposed framework is effective and able to outperform several baseline methods with regard to both text summarization systems and automatic evaluation metrics when tested on a publicly available dataset.
Highlights
With the tremendous and growing of smart phones and web technologies, the amount of text data on the web is increasing exponentially
Torres-Moreno [2] provided six reasons why automatic text summarization system is needed: (1) it creates summaries that reduce users reading time; (2) when researching documents, the generated summaries make the selection process easier; (3) it improves the effectiveness of indexing; (4) automatic summarization system is less biased than human summarizers; (5) the produced summaries are useful in question-answering systems; and (6) using automatic summarization system enables commercial abstract services to increase the number of texts they are able to process
Our research questions are as follows: RQ1: For salient top-n sentences determination, how can we leverage publicly available pre-trained word embedding models? In order to answer this research question, we develop a system called word embedding based text summarization (WETS for short), and compare with the baseline systems
Summary
With the tremendous and growing of smart phones and web technologies, the amount of text data on the web is increasing exponentially. Users should spend more time to find relevant information This has inspired the development of automatic text summarization systems for producing a concise summary that preserves core idea of the original document [1]. Any automatic text summarization system is intended for distilling most relevant information from the original document to create a shortened version. Torres-Moreno [2] provided six reasons why automatic text summarization system is needed: (1) it creates summaries that reduce users reading time; (2) when researching documents, the generated summaries make the selection process easier; (3) it improves the effectiveness of indexing; (4) automatic summarization system is less biased than human summarizers;. (5) the produced summaries are useful in question-answering systems; and (6) using automatic summarization system enables commercial abstract services to increase the number of texts they are able to process.
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