@phdthesis{Keller2022, type = {Bachelor Thesis}, author = {J{\"u}ri Keller}, title = {Automated statement extraction from press briefings}, url = {https://nbn-resolving.org/urn:nbn:de:hbz:79pbc-opus-18572}, pages = {47}, year = {2022}, abstract = {Scientific press briefings are a valuable information source. They consist of alternating expert speeches and answers to questions from the audience. Therefore, they can contribute to fact based media coverage. Even though press briefings are highly informative, filtering this information and extracting relevant statements is challenging and time-consuming. To support this task, an automated statement extraction system is proposed. Claims are identified as a core component of a statement and used as the main feature to identify statements in press briefing transcripts. The statement extraction task is formulated as a two-step procedure. First, claim sentences are identified with a single label multi-class sequence classification. Second, the sentences are filtered to improve the coherence and assess the length of the statements. The results provided indicate that claim detection can be used to identify statements in press briefings. While many statements can be extracted automatically with this system, these are not always as coherent as needed to be understood without context and may need further assessment through knowledgeable persons.}, language = {de} }