Exploration of Cross-lingual Summarization for K annada- E nglish L anguage Pair (2020)

Abstract: Cross-lingual summarization(CLS) is the process of generating a summary in one particular language for a source document in a different language. Low resource languages like Kannada greatly benefit from such systems because they help in delivering a concise representation of the same information in a different popular language. We propose a novel dataset generation pipeline and a first of its kind dataset that will aid in CLS for Kannada-English language pair. This work is also an attempt to inspect the existing systems and extend them to the Kannada-English language pair using our dataset.

## introduction with the advancement in technology, language should not be a barrier to gain knowledge when everyone has access to the same information. the need for an intelligent system that understands and analyzes text in low resource languages while delivering concise representation of the text in a well known language without losing out on any information is paramount. cross-lingual summarization systems fit these requirements perfectly. for a given document in the source language, the primary objective of the system is to generate meaningful summary in the target language (different from source language) without discarding any crucial information. these systems extend resources available in low resources languages like kannada to everyone who can understand a well known language such as english. monolingual summarization is extensively studied due to the availability of resources while crosslingual summarization systems are relatively unpopular due to the lack of training corpus. to tackle this, we present a one of it's kind dataset for training a cls system for kannada-english language pair along with our experimentation. a robust pipeline for the generation of dataset is designed using round trip translation strategy from @xcite and a back translation strategy proposed by @xcite using the newsroom dataset from @xcite as our primary backbone. we have successfully extended several methods from @xcite , @xcite and some baselines from @xcite to kannada-english language pair using our dataset. section 2 describes the dataset generation pipeline and the experimentation carried out. the results and inferences are discussed in section 3 while the conclusions are briefed in section 4. ## results the rouge scores of the round trip translated corpus with the records from the newsroom dataset were recorded. this was done in order to inspect the records and set an appropriate threshold to filter out the noisy records. removing these unwanted records helped in increasing the quality of the dataset. the same observation was recorded for the summaries as well. this enabled the dataset to be more flexible with the interchangeability of the source and destination languages. the threshold was decided after inspecting the distribution of the rouge scores between round trip translated data and the original summary. our experimentation of manual inspection by evenly sampling 30% of the records by 3 volunteers yielded 0.24 with variance of 0.03, the next stages of the experiments were carried forward with this threshold in mind. figures 3a and 3b helped in filtering the records by providing the overview of the distribution. the attempt to check for redundancy in the outputs between the results exhibited by the simfusion and corank algorithms were done. although both of them follow a graphical approach, figure 3c proved that the information captured by these algorithms were quite different. there was very little correlation between the scores of two algorithms for the same set of records, this implies that an ensemble of both these models can provide maximum outreach. the rouge scores achieved from the experimentation with the different cls methods are depicted in ## conclusion in this paper, we proposed a first of its kind dataset that consists of content-summary mappings for the kannada-english language pair. since kannada is a low resource language, the dataset can aid for further experimentation on cross lingual applications. the newly designed dataset generation pipeline has also been proven to generate high quality records considering the cls methods that has been successfully extended to kannada-english language pair using the dataset.