BanditSum: Extractive Summarization as a Contextual Bandit

In this paper we introduce extreme summarization, a new single-document sum- marization task which is not amenable to extractive strategies and requires an ...







Language Independent Text Summarizer and Deep Self-Organizing ...
First, for the TD case, all the four supervised summarization methods tend to perform on par with one another when evaluated with the various ROUGE metrics, ...
Topic Concentration in Query Focused Summarization Datasets - AAAI
Extractive approach selects highly meaningful sentences to form a summary while the abstractive approach interprets the original document and generates the.
A Study on Neural Network Modeling Techniques for Automatic ...
ABSTRACT- This research designs a system that, with the help of NLP, would sum up the YouTube video transcripts properly.
Autofolding for Source Code Summarization - People
We created a live demo of TASSAL [19] using the Play. Framework (https://www.playframework.com) to show- case how it can be used to summarize open-source Java.
Query-Based Single Document Summarization Using an Ensemble ...
In this paper we use a deep auto-encoder for extractive query-based summarization. We experiment with different input repre- sentations in order to overcome ...
Summary: Artificial Intelligence - A Modern Approach
Acting humanly: Turing test. ? Thinking humanly: Cognitive modeling approach, in line with cognitive science. ? Thinking rationally: The ?laws of thought? ...
Neural Methods for Sentiment Analysis and Text Summarization
Compared to extractive summarization, abstractive summaries are arguably a better approximation of human summaries (Cohn and Lapata, 2008).
TEXT SUMMARIZATION : AN OVERVIEW
A summary can be defined as a text that is produced from one or more texts, that contains a significant portion of the information in the original text(s), and.
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