Recently, encoder-decoder models are widely used in social media text summarization. However, these models sometimes select noise words in irrelevant sentences as part of a summary by error, thus declining the performance. In order to inhibit irrelevant sentences and focus on key information, we propose an effective approach by learning sentence weight distribution. In our model, we build a multi-layer perceptron to predict sentence weights. During training, we use the ROUGE score as an alternative to the estimated sentence weight, and try to minimize the gap between estimated weights and predicted weights. In this way, we encourage our model to focus on the key sentences, which have high relevance with the summary. Experimental results show that our approach outperforms baselines on a large-scale social media corpus.
Summarization approaches can be divided into two typical categories: extractive summarization @cite_16 @cite_11 @cite_4 @cite_5 @cite_6 and abstractive summarization @cite_15 @cite_14 @cite_3 @cite_12 @cite_1 . For extractive summarizations, most works usually select several sentences from a document as a summary or a headline. For abstractive summarization, most works usually encode a document into an abstractive representation and then generate words in a summary one by one. Most social media summarization systems belong to abstractive text summarizaition. Generally speaking, extractive summarization achieves better performance than abstractive summarization for long and normal documents. However, extractive summarization is not suitable for social media text which are full of noises and very short.
@cite_14 @cite_4 @cite_1 @cite_6 @cite_3 @cite_5 @cite_15 @cite_16 @cite_12 @cite_11
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One of the important Natural Language Processing applications is Text Summarization, which helps users to manage the vast amount of information available, by condensing documents' content and extracting the most relevant facts or topics included. Text Summarization can be classified according to the type of summary: extractive, and abstractive. Extractive summary is the procedure of identifying important sections of the text and producing them verbatim while abstractive summary aims to produce important material in a new generalized form. In this paper, a novel approach is presented to create an abstractive summary for a single document using a rich semantic graph reducing technique. The approach summaries the input document by creating a rich semantic graph for the original document, reducing the generated graph, and then generating the abstractive summary from the reduced graph. Besides, a simulated case study is presented to show how the original text was minimized to fifty percent. Extractive summarization is the strategy of concatenating extracts taken from a corpus into a summary, while abstractive summarization involves paraphrasing the corpus using novel sentences. We define a novel measure of corpus controversiality of opinions contained in evaluative text, and report the results of a user study comparing extractive and NLG-based abstractive summarization at different levels of controversiality. While the abstractive summarizer performs better overall, the results suggest that the margin by which abstraction outperforms extraction is greater when controversiality is high, providing a context in which the need for generation-based methods is especially great. Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines. Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.