While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge graph entities lack such textual descriptions. In this paper, we introduce a dynamic memory-based network that generates a short open vocabulary description of an entity by jointly leveraging induced fact embeddings as well as the dynamic context of the generated sequence of words. We demonstrate the ability of our architecture to discern relevant information for more accurate generation of type description by pitting the system against several strong baselines.
Generating entity descriptions is related to the task of text summarization. Most traditional work in this area was extractive in nature, i.e. it selects the most salient sentences from a given input text and concatenates them to form a shorter summary or presents them differently to the user @cite_9 . Abstractive summarization goes beyond this in generating new text not necessarily encountered in the input, as is typically necessary in our setting. The surge of sequence-to-sequence modeling of text via LSTMs naturally extends to the task of abstractive summarization by training a model to accept a longer sequence as input and learning to generate a shorter compressed sequence as a summary.
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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. 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. Text summary helps in understanding the content of a text without having to read the contents of the text as a whole. Automatic text summarization can be used to summarize the text easier. In this paper a frequent term based text summarization for Bahasa Indonesia is designed and implemented in java. The proposed system generates a summary for a given input document based on identification and extraction of important sentences in the document. The system counts nouns and verbs term frequency because they are considered as the most representative to the content of the text. The system also integrated to statistical approach with two underlying concepts such as title of the news article and location of the sentence. The generated summaries were compared with human generated summaries. Precision, recall and f-measure ratio are used to evaluate the accuracy of the generated summary. Assessment of the system summary result quality by respondents is also done by giving a value from 1 to 100. Based on the experimental results, the system is able to produce an effective summary with the average f-measure of 78 , at the compression rate of 30 . The average value of the quality of system summary result provided by respondents is 83,3 Query Focused Summarization (QFS) has been addressed mostly using extractive methods. Such methods, however, produce text which suffers from low coherence. We investigate how abstractive methods can be applied to QFS, to overcome such limitations. Recent developments in neural-attention based sequence-to-sequence models have led to state-of-the-art results on the task of abstractive generic single document summarization. Such models are trained in an end to end method on large amounts of training data. We address three aspects to make abstractive summarization applicable to QFS: (a)since there is no training data, we incorporate query relevance into a pre-trained abstractive model; (b) since existing abstractive models are trained in a single-document setting, we design an iterated method to embed abstractive models within the multi-document requirement of QFS; (c) the abstractive models we adapt are trained to generate text of specific length (about 100 words), while we aim at generating output of a different size (about 250 words); we design a way to adapt the target size of the generated summaries to a given size ratio. We compare our method (Relevance Sensitive Attention for QFS) to extractive baselines and with various ways to combine abstractive models on the DUC QFS datasets and demonstrate solid improvements on ROUGE performance.