3 research outputs found
Summarizing Indian Languages using Multilingual Transformers based Models
With the advent of multilingual models like mBART, mT5, IndicBART etc.,
summarization in low resource Indian languages is getting a lot of attention
now a days. But still the number of datasets is low in number. In this work, we
(Team HakunaMatata) study how these multilingual models perform on the datasets
which have Indian languages as source and target text while performing
summarization. We experimented with IndicBART and mT5 models to perform the
experiments and report the ROUGE-1, ROUGE-2, ROUGE-3 and ROUGE-4 scores as a
performance metric
XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages
Lack of encyclopedic text contributors, especially on Wikipedia, makes
automated text generation for low resource (LR) languages a critical problem.
Existing work on Wikipedia text generation has focused on English only where
English reference articles are summarized to generate English Wikipedia pages.
But, for low-resource languages, the scarcity of reference articles makes
monolingual summarization ineffective in solving this problem. Hence, in this
work, we propose XWikiGen, which is the task of cross-lingual multi-document
summarization of text from multiple reference articles, written in various
languages, to generate Wikipedia-style text. Accordingly, we contribute a
benchmark dataset, XWikiRef, spanning ~69K Wikipedia articles covering five
domains and eight languages. We harness this dataset to train a two-stage
system where the input is a set of citations and a section title and the output
is a section-specific LR summary. The proposed system is based on a novel idea
of neural unsupervised extractive summarization to coarsely identify salient
information followed by a neural abstractive model to generate the
section-specific text. Extensive experiments show that multi-domain training is
better than the multi-lingual setup on average
GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering
Commonsense question-answering (QA) methods combine the power of pre-trained
Language Models (LM) with the reasoning provided by Knowledge Graphs (KG). A
typical approach collects nodes relevant to the QA pair from a KG to form a
Working Graph (WG) followed by reasoning using Graph Neural Networks(GNNs).
This faces two major challenges: (i) it is difficult to capture all the
information from the QA in the WG, and (ii) the WG contains some irrelevant
nodes from the KG. To address these, we propose GrapeQA with two simple
improvements on the WG: (i) Prominent Entities for Graph Augmentation
identifies relevant text chunks from the QA pair and augments the WG with
corresponding latent representations from the LM, and (ii) Context-Aware Node
Pruning removes nodes that are less relevant to the QA pair. We evaluate our
results on OpenBookQA, CommonsenseQA and MedQA-USMLE and see that GrapeQA shows
consistent improvements over its LM + KG predecessor (QA-GNN in particular) and
large improvements on OpenBookQA