2 research outputs found
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
We introduce extreme summarization, a new single-document summarization task
which does not favor extractive strategies and calls for an abstractive
modeling approach. The idea is to create a short, one-sentence news summary
answering the question "What is the article about?". We collect a real-world,
large-scale dataset for this task by harvesting online articles from the
British Broadcasting Corporation (BBC). We propose a novel abstractive model
which is conditioned on the article's topics and based entirely on
convolutional neural networks. We demonstrate experimentally that this
architecture captures long-range dependencies in a document and recognizes
pertinent content, outperforming an oracle extractive system and
state-of-the-art abstractive approaches when evaluated automatically and by
humans.Comment: 11, 2018 Conference on Empirical Methods in Natural Language
Processing, EMNLP 201
Abstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTM
Text summarization is a fundamental task in natural language processing that
aims to condense large amounts of textual information into concise and coherent
summaries. With the exponential growth of content and the need to extract key
information efficiently, text summarization has gained significant attention in
recent years. In this study, LSTM and pre-trained T5, Pegasus, BART and
BART-Large model performances were evaluated on the open source dataset (Xsum,
CNN/Daily Mail, Amazon Fine Food Review and News Summary) and the prepared
resume dataset. This resume dataset consists of many information such as
language, education, experience, personal information, skills, and this data
includes 75 resumes. The primary objective of this research was to classify
resume text. Various techniques such as LSTM, pre-trained models, and
fine-tuned models were assessed using a dataset of resumes. The BART-Large
model fine-tuned with the resume dataset gave the best performance