The manual extraction \
of relevant information from text documents is a \
time-consuming task for any policy analysts. As the amount and length of \
public policy documents in relation to sustainable development (such as \
National Development Plans and Nationally Determined Contributions) \
continuously increases, a major challenge for policy action tracking – the \
evaluation of stated goals and targets and their actual implementation on \
the ground – arises. Luckily, Artificial Intelligence (AI) and Natural \
Language Processing (NLP) methods can help in shortening and easing this \
task for policy analysts.
',
unsafe_allow_html=True)
intro = """
For this purpose, the United Nations Sustainable Development Solutions \
Network (SDSN) and the Deutsche Gesellschaft für Internationale \
Zusammenarbeit (GIZ) GmbH are collaborating since 2021 in the development \
of an AI-powered open-source web application that helps find and extract \
relevant information from public policy documents faster to facilitate \
evidence-based decision-making processes in sustainable development and beyond.
The collaboration aims to determine the potential of NLP methods for \
tracking policy implementation and coherence in the context of the \
Sustainable Development Goals (SDGs) and the Paris Climate Agreement. \
Nationally determined contributions (NDCs) will serve as a starting \
point for the analysis and evaluation in a specific national context. \
Under the Paris Climate Agreement, NDCs embody the efforts of each \
country to reduce national emissions and thus contribute to the \
achievement of the long-term goals of the Agreement – to increase the \
ability to adapt to adverse impacts of climate change and foster \
climate resilience and low greenhouse gas emissions development, in a \
manner that does not threaten food production. The Paris Climate \
Agreement (Article 4, Paragraph 2)1 requires each Party to prepare, \
communicate and maintain successive NDCs. Thus, they serve as a \
comparable, accessible, and widely acknowledged starting point for \
analysis. However, the agreed and communicated goals and measures must \
also be reflected in national strategies, statements, and other \
government publications to be implemented timely, as well as effectively.\
At best, the activities and measures should have an allocated budget. \
Given the complexity, the manual evaluation of policy documents and \
other publications has been very time-consuming and has presented a \
significant challenge for policy analysts and makers alike. In \
consequence, the open-source web application aims to support the process\
through suitable AI-powered and NLP methods. In the following, the \
application’s functionalities are explained in more detail.
- Analizing the policy document
- finding SDG related content
- Make it searchable
- compare it to the national NDC
""" st.markdown(intro, unsafe_allow_html=True) st.image("docStore/img/pic1.png") #st.subheader("Methodology") #st.write("Each sentence in the generated answer ends with a coloured tooltip; the colour ranges from red to green. " # "The tooltip contains a value representing answer sentence similarity to a specific sentence in the " # "Wikipedia context passages retrieved. Mouseover on the tooltip will show the sentence from the " # "Wikipedia context passage. If a sentence similarity is 1.0, the seq2seq model extracted and " # "copied the sentence verbatim from Wikipedia context passages. Lower values of sentence " # "similarity indicate the seq2seq model is struggling to generate a relevant sentence for the question " # "asked.") #st.image("wikipedia_answer.png", caption="Answer with similarity tooltips")