Summary of Findings

A new dataset with abstractive dialogue summaries.

DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues with corresponding manually labeled summaries and topics. This work is accepted by ACL findings 2021. You may find the paper here: https://arxiv.org/pdf/2105.06762.pdf. If you want to use our dataset, please cite our paper.

CNN/Daily Mail is a dataset for text summarization. Human generated abstractive summary bullets were generated from news stories in CNN and Daily Mail websites as questions (with one of the entities hidden), and stories as the corresponding passages from which the system is expected to answer the fill-in the-blank question.

C4 is a colossal, cleaned version of Common Crawl's web crawl corpus. It was based on Common Crawl dataset: https://commoncrawl.org. It was used to train the T5 text-to-text Transformer models. The dataset can be downloaded in a pre-processed form from allennlp.

Transformers are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.

Abstract. Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches that could further enhance their efficiency. In this direction, this work presents a novel framework that combines sequence-to-sequence neural-based text summarization along with structure and semantic-based methodologies.

Abstractive Summarization of Text using Encoder-Decoder Based Architecture K.S. Agilan, R. Aswathaman, R. Harinisri, M. Salomi Abstract The internet keeps bringing tons and tons of information to its users on a daily basis - reading everything can consume months or years and sometimes even decades.

Machine Learning Text summarization is a common problem in the fields of machine learning and natural language processing (NLP). In this article, we'll explore how to create a simple extractive text summarization algorithm. Have you ever summarized a lengthy document into a short paragraph? How long did you take?

This article was published as a part of the Data Science Blogathon. Text Summarisation is an important Natural Language Processing(NLP) task. Text summarisation involves condensing a larger text into smaller sizes by preserving the meaning to convey the core message of the text.

It can take a person days, or even weeks, to sift through a 50-page technical document, filter out irrelevant material, and write a complete summary of the text without compromising on correctness. When you consider sensitive legal and financial documents, there is no room for error including leaving an important detail out.

Ever feel like you don't have enough time to read everything that you want to? What if you could run a routine that summarized documents for you, whether it's your favorite news source, academic articles, or work-related documents? Text summarization is a Natural Language Processing (NLP) task that summarizes the information in large texts for quicker consumption without losing vital information.

Before we move on to the complicated concepts, let us quickly understand Text Summarization in Python. Here is the definition for the same.

"I don't want a full report, just give me a summary of the results". I have often found myself in this situation - both in college as well as my professional life. We prepare a comprehensive report and the teacher/supervisor only has time to read the summary. Sounds familiar?

Pre-training is a hot topic in NLP research and models like BERT and GPT have definitely delivered exciting breakthroughs. The challenge is in upping our game in finer sequence to sequence based language generation tasks. Enter MASS. Click the link in our bio to learn more!

With the rapid development of the Internet, the massive amount of web textual data has grown exponentially, which has brought considerable challenges to downstream tasks, such as document management, text classification, and information retrieval. Automatic text summarization (ATS) is becoming an extremely important means to solve this problem.

In this article, we introduce a few ways in which we can use recent advances in natural language processing and deep learning to summarize text. The techniques shown here have wide applications - from automatically extracting meaning from user reviews, to legal contract analysis, optimizing SEO strategies, financial analysis, extracting important information from electronic medical records, etc.

PDF version on arXiv For over a quarter of century we have been able to search the web by querying a search engine using a couple of relevant keywords. Without such a tool the internet would be nothing but useless garbage dump of data.