Paper: https://arxiv.org/abs/1706.03946 Abstract: Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.

1. Best and worst
summary
sentences in
each paper
found with an
oracle, used as
training data
A Supervised Approach to Extractive Summarisation of Scientific Papers
Ed Collins, Isabelle Augenstein, Sebastian Riedel
{edward.collins.13 | i.augenstein | s.riedel}@ucl.ac.uk
Select the sentences
from within a paper
which best summarise
that paper. Binary
classification task -
each sentence
classified as either
summary or not.
The Task
Challenges
Data and Evaluation Setup
Length
Data
Approach
Features in Order of Utility:
• AbstractROUGE - ROUGE-L
score of sentence and
abstract, taking inspiration
from other work on
summarising scientific papers
• TF-IDF
• Keyphrase Score
• Title Score
• Document TF-IDF
• Sentence Length
• Section Sentence Occurred In
• Numeric Count - number of
numbers in the sentence
Results & Conclusion
• Classifiers which use a neural network to
read text suffer no significant changes to
performance if a feature is missing
Code: https://github.com/EdCo95/scientific-paper-summarisation
Papers are long - a lot of information to
summarise
No suitable datasets available to train
data-hungry learning algorithms
• Remaining challenges are to encode the whole
document, rather than just a sentence, with
neural networks to better understand the global
context of each sentence
• Significantly outperforms
many baselines
• Classifiers trained on the
automatically extended
dataset performed better
than those trained
without it
400K sentences
with classifications
Train
Test
(accuracy)
263K 130K
150 full papers
Test
(summary quality)
Using ROUGE-L
10K Papers
Each with highlight statements.
Assume highlights are good summaries
even out of context.