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Read Top News First: A Document Reordering Approach for Multi-Document News Summarization

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TLDR
The authors propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them, which makes the salient content easier to learn by the summarization model.
Abstract
A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document. However, this method neglects the relative importance of documents. We propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them. The reordering makes the salient content easier to learn by the summarization model. Experiments show that our approach outperforms previous state-of-the-art methods with more complex architectures.

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Journal ArticleDOI

Multi-Document News Web Page Summarization Using Content Extraction and Lexical Chain Based Key Phrase Extraction

TL;DR: This article presented a method for summarizing multi-document news web pages based on similarity models and sentence ranking, where relevant sentences are extracted from the original article, where they collected from five news websites that cover the same topic and event.
Proceedings ArticleDOI

AuxPOS: Improving Grammatical Correctness with Big Data Based Text Summarization

TL;DR: This paper proposed a grammar-aware text summarization method by incorporating POS constraints to guide the decoding process, along with auxiliary embeddings including POS, Lemmatization and Named Entity Recognition features in encoder.
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