A Zipf-Like Distant Supervision Approach for Multi-document Summarization Using Wikinews Articles

Bravo-Márquez, Felipe; Manriquez, Manuel

doi:10.1007/978-3-642-34109-0_15

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…In these methods, a human user decides which parameters are important for text summary and accordingly the summary is generated. Bravo-Marquez and Manriquez trained ranking functions using linear regressions and ranking SVMs, which are also combined using Borda count [9]. Top ranked sentences are concatenated and used to build summaries, which are compared with the first sentences of the distant summary using ROUGE evaluation measures [10].…”

Section: C) Supervised Approachesmentioning

confidence: 99%

A Survey of Unstructured Text Summarization Techniques

Elfayoumy¹,

Thoppil²

2014

IJACSA

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Abstract-Due to the explosive amounts of text data being created and organizations increased desire to leverage their data corpora, especially with the availability of Big Data platforms, there is not usually enough time to read and understand each document and make decisions based on document contents. Hence, there is a great demand for summarizing text documents to provide a representative substitute for the original documents. By improving summarizing techniques, precision of document retrieval through search queries against summarized documents is expected to improve in comparison to querying against the full spectrum of original documents.Several generic text summarization algorithms have been developed, each with its own advantages and disadvantages. For example, some algorithms are particularly good for summarizing short documents but not for long ones. Others perform well in identifying and summarizing single-topic documents but their precision degrades sharply with multi-topic documents. In this article we present a survey of the literature in text summarization. We also surveyed some of the most common evaluation methods for the quality of automated text summarization techniques. Last, we identified some of the challenging problems that are still open, in particular the need for a universal approach that yields good results for mixed types of documents.

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Section: C) Supervised Approachesmentioning

confidence: 99%

A Survey of Unstructured Text Summarization Techniques

Elfayoumy¹,

Thoppil²

2014

IJACSA

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Modelling on web summarisation based on structure analysis and vectorisation similarity

Gao

Xia

Ganesan³

2013

IJMIC

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Low-Resource Neural Headline Generation

Tilk¹,

Alumäe²

2017

Proceedings of the Workshop on New Frontiers in Summarization

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Recent neural headline generation models have shown great results, but are generally trained on very large datasets. We focus our efforts on improving headline quality on smaller datasets by the means of pretraining. We propose new methods that enable pre-training all the parameters of the model and utilize all available text, resulting in improvements by up to 32.4% relative in perplexity and 2.84 points in ROUGE.

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A Zipf-Like Distant Supervision Approach for Multi-document Summarization Using Wikinews Articles

Cited by 3 publications

References 14 publications

A Survey of Unstructured Text Summarization Techniques

A Survey of Unstructured Text Summarization Techniques

Modelling on web summarisation based on structure analysis and vectorisation similarity

Low-Resource Neural Headline Generation

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