The sequence-level reconstructor reconstructs the whole document from the hidden layer of the target summary, while the word embedding-level one rebuilds the ...
The assumption that inverse document frequency (IDF) measures how important a word is is further leverage the IDF weights in the embedding-level ...
Abstractive Document Summarization with Word Embedding ...
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... Timeline summarization (TLS) is designed to extract sentences that describe an evolutionary story from a massive amount of web articles with respect to a ...
The sequence-level reconstructor reconstructs the whole document from the hidden layer of the target summary, while the word embedding-level one rebuilds the ...
Neural sequence-to-sequence (Seq2Seq) models and BERT have achieved substantial improvements in abstractive document summarization (ADS) without and with pre- ...
Sep 10, 2023 · This article provides guidance on assessing embedding models and selecting the right one to match the needs of different summarization tasks.
Missing: Reconstruction. | Show results with:Reconstruction.
This work presents a novel framework that combines sequence-to-sequence neural-based text summarization along with structure and semantic-based methodologies.
Missing: Reconstruction. | Show results with:Reconstruction.
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To that end, they introduce a novel, straightforward yet highly effective method for combining multiple types of word embeddings in a single model, leading to ...
This paper explores the complex field of text summarization in Natural Language Processing (NLP), with particular attention to the development and importance ...
Sep 4, 2024 · A generated abstractive summary provides an accurate, concise, and easy-to-understand representation of the source text by fulfilling these ...