# Attention based Sentence Extraction from Scientific Articles using Pseudo-Labeled data

arXiv: https://arxiv.org/abs/1802.04675 · HTML: https://mlsanity.com/paper/1802.04675/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks](https://mlsanity.com/paper/2212.02805/) (2022)

## Abstract (arXiv)

In this work, we present a weakly supervised sentence extraction technique for identifying important sentences in scientific papers that are worthy of inclusion in the abstract. We propose a new attention based deep learning architecture that jointly learns to identify important content, as well as the cue phrases that are indicative of summary worthy sentences. We propose a new context embedding technique for determining the focus of a given paper using topic models and use it jointly with an LSTM based sequence encoder to learn attention weights across the sentence words. We use a collection of articles publicly available through ACL anthology for our experiments. Our system achieves a performance that is better, in terms of several ROUGE metrics, as compared to several state of art extractive techniques. It also generates more coherent summaries and preserves the overall structure of the document.