Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Memory and Knowledge Augmented Language Models for Inferring Salience in
  Long-Form Stories   Measuring event salience is essential in the understanding of stories. This
paper takes a recent unsupervised method for salience detection derived from
Barthes Cardinal Functions and theories of surprise and applies it to longer
narrative forms. We improve the standard transformer language model by
incorporating an external knowledgebase (derived from Retrieval Augmented
Generation) and adding a memory mechanism to enhance performance on longer
works. We use a novel approach to derive salience annotation using
chapter-aligned summaries from the Shmoop corpus for classic literary works.
Our evaluation against this data demonstrates that our salience detection model
improves performance over and above a non-knowledgebase and memory augmented
language model, both of which are crucial to this improvement.

15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th