Экстрактивті мәтіндік қорытындылау

Recent studies on extractive text summariza- tion formulate it as a combinatorial optimiza- tion problem such as a Knapsack Problem, a Maximum Coverage Problem or a Budgeted Median Problem. These …...

The most successful approaches to extrac- tive text summarization seek to maximize bigram coverage subject to a budget con- straint. In this work, we propose instead to maximize semantic volume. …...

. We report a series of experiments with different semantic models on top of various statistical models for extractive text summarization. Though statistical models may better cap- ture word co-occurrences …...

AbstractSentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations. There are lots of redundant phrases between extracted sentences, …...

AbstractExtractive text summarization seeks to extract indicative sentences from a source document andassemble them to form a summary. Selecting salient but not redundant sentences has alwaysbeen the main challenge. Unlike …...

Recent researches have demonstrated that BERT shows potential in a wide range of natural language processing tasks. is adopted as an encoder for many state-It of-the-art automatic summarizing sys- tems, …...

AbstractExtractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sentence embedding plays …...

AbstractLegal documents tend to be large in size. In this paper, we provide an experiment with attention-based approaches complemented by certain document processing techniques for judgment prediction. For the prediction …...