ROUGE бағасы

This paper presents a novel approach to automatic captioning of geo-tagged images by summarizing multiple web- documents that contain information re- lated to an image’s location. The summa- rizer is …...

The current state-of-the-art single- document summarization method gen- erates a summary by solving a Tree Knapsack Problem (TKP), which is the problem of finding the optimal rooted sub- tree of …...

In concept-based summarization, sentence selection is modelled as a budgeted maxi- mum coverage problem. As this problem is NP-hard, pruning low-weight concepts is required for the solver to find optimal …...

This paper presents a problem-reduction approach to extractive multi-document summarization: we propose a reduction to the problem of scoring individual sen- tences with their ROUGE scores based on supervised learning. …...

AbstractText summarization is considered as a challenging task in the NLP community. The availability of datasets for the task of multilingual text summarization is rare, and such datasets are difficult …...

AbstractSummarizing texts is not a straightforward task. Before even considering text summarization, one should determine what kind of summary is expected. How much should the information be compressed? Is it …...

AbstractIn this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from Liputan6.com, an online news portal, and obtain 215,827 document–summary pairs. We leverage pre-trained language models to …...

AbstractExtracting the most important part of legislation documents has great business value because the texts are usually very long and hard to understand. The aim of this article is to …...

AbstractUsers of medical question answering systems often submit long and detailed questions, making it hard to achieve high recall in answer retrieval. To alleviate this problem, we propose a novel …...

AbstractThis paper presents an efficient graph-enhanced approach to multi-document summarization (MDS) with an encoder-decoder Transformer model. This model is based on recent advances in pre-training both encoder and decoder on …...

AbstractThis paper explores a variant of automatic headline generation methods, where a generated headline is required to include a given phrase such as a company or a product name. Previous …...

AbstractMultimodal abstractive summarization (MAS) models that summarize videos (vision modality) and their corresponding transcripts (text modality) are able to extract the essential information from massive multimodal data on the Internet. …...

AbstractPrevious studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them. They also considered date selection and event …...

AbstractCurrent medical question answering systems have difficulty processing long, detailed and informally worded questions submitted by patients, called Consumer Health Questions (CHQs). To address this issue, we introduce a medical …...

AbstractText summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained language models (LM) are unable to efficiently process long text for many summarization …...

AbstractMulti-hop question generation (QG) is the process of generating answer related questions, which requires aggregating multiple pieces of information and reasoning from different parts of the texts. This is opposed …...

AbstractIn this work, we study the importance of content frequency on abstractive summarization, where we define the content as “semantic units.” We propose a two-stage training framework to let the …...

AbstractMulti-Document Summarization (MDS) commonly employs the 2-stage extract-then-abstract paradigm, which first extracts a relatively short meta-document, then feeds it into the deep neural networks to generate an abstract. Previous work …...